David de Jesús Mastrascusa González emissions Energy and baking performance of alternative cooking technologies aiming to reduce CO School of Engineering and Sciences Instituto Tecnológico y de Estudios Superiores de Monterrey

Instituto Tecnológico y de Estudios Superiores de Monterrey

Campus Monterrey

School of Engineering and Sciences

Energy and baking performance of alternative cooking technologies aiming to reduce CO

emissions

A thesis presented by

David de Jesús Mastrascusa González

Submitted to the

School of Engineering and Sciences

in partial fulfillment of the requirements for the degree of Master of Science

In

Engineering Science

Monterrey Nuevo León, May 29

, 2020

Dedication

A Dios, a mi familia, a mis amigos, a mis profesores con título y a mis profesores sin título (el señor del raspao , la se ora de la mesa de fritos, el vendedor de BonIce ), esos que le ense an a uno que la vida es dura y se trabaja, pero se goza, porque uno es el que le pone la actitud.

Acknowledgments

This work was possible thanks to the resources, infrastructure and support provided by Tecnológico de Monterrey, Energy and Climate Change Research group, CONACyT and Welbilt.

Thanks to my advisor PhD. Jos Ignacio and Co-Advisor PhD. Olga Patricia Vázquez for the continuous support during my studies and research, for their patience, motivation, enthusiasm, and immense knowledge. Their guidance helped me in all the time of research and in times of need.

I gratefully acknowledge the contributions to my thesis from the committee, PhD. Esther Pérez Carrillo, PhD. Alejandro García Cuellar and PhD. Natalia Navarrete.

To all my fellow colleagues at the Energy and Climate Change Research group and the master s in engineering science program for their support in my personal and professional growth.

And last but not least to God for his grace and mercy.

Energy and baking performance of alternative cooking technologies aiming to reduce CO

emissions

by

David Mastrascusa González Abstract

Reducing energy consumption, cooking time and pollutant emissions in the restaurant industry has great economic and sustainability potential. In pizza cooking, the use of microwaves, superheated steam, infrared and magnetic induction, and some of their combinations was studied in order to reduce the cooking time. To guarantee the viability of the resulting product, the subjective properties of the cooked pizzas were analyzed objectively under instrumental techniques and grouped into cooking and physical quality characteristics. The pizzas produced by each technology were compared to a baseline pizza baked by an oven with hot air impingements widely used in restaurants. Energy consumption and CO2 emissions were estimated for each of the selected cooking methods in order to evaluate the environmental impact of the cooking time reduction. It was found that overall cooking time for the pizza was reduced by 50% when using IR in the last stage of the process. This resulted in a decrease of 27% in energy consumption and of 27.1% in CO2 emissions while retaining the desired quality properties of the pizza.

List of Figures

Figure 1. Photographic set configuration to evaluate BI and BF. The camera and the PC are not connected in real time, after taking the photos, they were copied via SD memory to the PC. ... 14 Figure 2. Position of the pizza trays inside the ovens. L, C and R Stands for Left, Center and Right for the Tech 2 and Tech 3 ovens. In the case of Tech1 the positions are A, B and A for left, right and center, respectively. The scheme of the thermocouples that were placed inside the ovens to measure temperature during cooking, is shown. ... 17 Figure 3. Image sequences of pizzas cooked at different times in the three ovens, in order to select the minimum cooking time, indicating their computed Browning Index. ... 18 Figure 4. Sequences of RVA viscoamylograms of bread cooked in the three different ovens (a).

Viscoamylograms of pizza samples cooked at three different positions in the Tech1 (b) and Tech2 (c) ovens, respectively. The horizontal gray line is added for reference only. The vertical lines that produce the shading are the error bars. ... 19 Figure 5. Pasting profile of flour and dough (fermented and unfermented). ... 20 Figure 6. Micrographs of the bread crumbs used in the RVA tests. The micrographs were taken under 700X magnification, 10kV, High Vacuum Mode. a) Tech1. b) Tech2. c) Tech3. ... 21 Figure 7. Temperature profiles inside the oven cavity, during cooking. a)Tech1, b)Tech2, c)Tech3.

... 23 Figure 8. Average of sensory analysis values. Only Color is significantly different (p-value = 0.022). ... 24 Figure 9. Image sequences of pizzas baked at different times and temperatures with only HA, in order to select the cooking time and temperature of the reference pizza, indicating their computed Browning Index... 26 Figure 10. Temperature profile of the reference pizza (260 °C, 6 min: 30 sec). ... 27 Figure 11. The selection ranges for the decision matrix. ... 28 Figure 12. Diagram of the implemented technologies combination. a) Microwave and Air

Impingement, b) Superheated Steam and Air Impingement, c) Induction, Infrared and Air

Impingement, and d) Infrared and Air Impingement. ... 28 Figure 13. The sequence of RVA visco-amylograms of bread cooked with AI, MW, IR, IND, and STM. Letters represent the different combinations: A) 6min 30s AI (reference or baseline), B) 3min 15 AI/IR, D) 26min STM 6min 30s AI, E) 35s IND 3min 00s AI, F) 10s MW 4min 20s AI, G) 30s 3min 00s AI, y H) 6min 30s MW... 34 Figure 14. Micrographs of the bread crumb baked with MW (sample H), MW/IA (sample G) and only IA (sample A). The micrographs were taken under 300X for a to f, and 25X magnification for g to i, 10kV, High Vacuum Mode. ... 35

List of Tables

Table 1. Characteristics of the ovens used to cook pizzas. The fan velocity was set as default in

each oven. Only Tech3 oven has a tray turning mechanism. ... 16

Table 2. Coordinates for each point inside the Figure 2. The coordinates points are (x, y, z). ... 17

Table 3. RVA values of the pizza samples baked in three different ovens. ... 20

Table 4. Physical characteristics of pizza cooked in 3 different ovens. ... 22

Table 5. Method to evaluate pizza quality and representative values of acceptable simple pizzas in northern Mexico, after baking. In this range, untrained panelists did not find differences. All data is presented in terms of mean ± 95% confidence interval. ... 25

Table 6. Characteristics of the conveyorized electric oven Digital Countertop Impinger (DCTI) used in this study. ... 26

Table 7. Positive-negative matrix for the selection of potential technologies (+, better; 0, same; -, worse). ... 29

Table 8. Hot air usage time factors for the production of one pizza at different temperatures. ... 32

Table 9. Technology parameters that produce good quality pizzas for each combination A to H. .. 33

Table 10. RVA values of the pizza samples baked with AI, MW, IR, IND, and STM. Values followed by the same letter (a, b, c) are not significantly different at 95% confidence interval. ... 35

Table 11. Physical characteristics for pizza baked with AI, MW, IR, IND, and STM. ... 36

Table 12. Energy consumption and CO2 emission parameters for pizza baked with AI, MW, IR, IND and STM... 39

Contents

Abstract ... v

List of Figures ... vi

List of Tables ... vii

Chapter 1... 9

Introduction ... 9

Chapter 2... 12

Metrics and methods to evaluate the pizza quality ... 12

2.1 Pizza preparation ... 12

2.2 Pizza characterization ... 12

Chapter 3... 16

Determination of the values for the metrics that define a well acceptable pizza ... 16

3.1 Results of the methodology to obtain a well acceptable pizza ... 17

Chapter 4... 26

Technology identification ... 26

4.1 Technology selection and experimental setup ... 27

4.2 Calculation of consumed energy and CO

emissions ... 31

Chapter 5... 33

Technology evaluation ... 33

5.1 Measurements of the pizza cooking quality parameters ... 33

5.2 Measurement of the pizza quality parameters ... 36

5.3 Energy and CO

emission evaluation ... 38

Chapter 6... 40

Conclusions ... 40

Appendix A... 42

Abbreviations and acronyms ... 42

Appendix B ... 43

Variables and Symbols ... 43

Bibliography ... 44

Published papers ... 50

Curriculum Vitae ... 51

Chapter 1

Introduction

Motivation

The pollution circumstances of the world have boosted an opportunity field for the design of equipment with new mechanisms to reach out better efficiencies (Orsat & Raghavan, 2014). The restaurant industry uses five to ten times more energy per square foot than other commercial spaces (Johnson, Fisher, Meister, & Huestis, 2013). Thus, the energy appliances have been related with up to 72% of carbon emissions in some countries and due to the burning of the fossils fuels carbon monoxide, carbon dioxide and volatile organic compounds (VOCs) (Kuehn, Olson, Ramsey, &

Rocklage, 2009; Pask & France, 2016). Cooking ovens spend around 46% of energy in commercial kitchens (S. Mudie, Essah, Grandison, & Felgate, 2014).

The optimization of cooking appliances to get more efficient processes has effects in three areas:

environmental, economic, and social. Regarding the economic impact improving efficiency either by reducing energy consumption or reducing cooking time is of interest for restaurants and manufacturers because it implies a direct operating cost reduction and quality improvement of the product. The social impact is related to a decrease in the emissions of harmful compounds and an increase in employment opportunities (Pask & France, 2016). The cooking time reduction impacts the customers' satisfaction and the productivity of the cooking units.

Problem Statement and Context

In the baking industry, considerable reductions in processing time and improvements in the product quality have been attained with the use of continuous, hot air impingement ovens (Banooni et al., 2008; Shevade, Rahman, & Guldiken, 2019; Soleimani Pour-Damanab, Jafary, & Rafiee, 2014).

Microwave, even when considered advantageous in cooking time and energy saving, has only be used for home appliances, since its heating non-uniformity adversely affected both the end product quality and safety, compared with conventional baking (Chandrasekaran, Ramanathan, & Basak, 2013; O.

Ozkoc, Sumnu, & Sahin, 2014). Many studies suggest the use of combined/hybrid ovens (Li &

Walker, 1996; Ploteau, Glouannec, Nicolas, & Magueresse, 2015). Assisted by different technologies (microwave, infrared, superheated steam,) not only help to uniformity in the heating, but also reduce the cooking time by the half compared with conventional heating processes.

The use of combined ovens can also generate energy savings of around 58% and emissions reductions of approximately 46% (Calado & Soares, 2012). However, the main difficulties of the application and implementation, when making this type of substitution are: a) it requires a high cost investment for assembling the systems, 2) it need robust theoretical or empirical models for process optimization of every technology, and 3) the analysis of the final characteristics of the food is commonly used for decision-making (Chizoba Ekezie, Sun, Han, & Cheng, 2017; Schenkel, Samudrala, & Hinrichs, 2013).

The last point is related to the characteristics demanded by customers. That is the quality of the product should be a starting point for the comparison of cooking technologies. Generally, the method of documenting the product quality is subjective for companies that produce ovens.

During baking, products undergo several changes that depend on the heating rate (Giovanelli G, Peri C, 1997; Lagrain, Brijs, & Delcour, 2008). An increase in the baking temperature and time generate first-order kinetics reactions such as the gelatinization and disruption of the starch granule (Probert

& Newborough, 1985), the solubilization of the protein and the formation of gluten (Lagrain et al., 2008; Purlis & Salvadori, 2007) , the generation of aroma compounds (Papasidero et al., 2016), and the browning of the crust (Gunasekaran, 2016; Schenkel et al., 2013; Wang & Sun, 2003; Zhang, Chen, Boom, & Schutyser, 2017). The higher the temperature, the greater the digestion of the starch granule and the insolubilization of the protein. The longer the time, the soluble amylose increases.

Low temperatures and long cooking time decrease the firmness of the crumb (Besbes, Le Bail, &

Seetharaman, 2016). All these transformations affect the final cooking and sensory quality of the product. But, the phenomenon with the highest impact is weight loss due to moisture loss (M. M.

Ureta, Olivera, & Salvadori, 2016).

For energy efficiency purposes, the control of water evaporation stage (23% to 26% of energy consumption) is crucial (Besbes et al., 2016; Deshlahra, Mehra, & Ghosal, 2009; Le-bail et al., 2010).

In a typical baking process, the oven temperature is set between 120 °C (the minimum requirement for crust browning) and 240°C (temperature reached by rapid ovens) (Besbes et al., 2016; M. Ureta et al., 2019). The application of heat start a water evaporation process on the surface of the bread.

This phenomena cause a gradient of moisture inside the dough which is remponsible of the water difussion from the center to the surface of the bread. This diffusion process is slower than the evaporation of the crust; but, the formation of crust and its thickness affects the loss of moisture from the inside of the bread (Soleimani Pour-Damanab et al., 2014). Before the crumb has reached 100°C, heat serves to increase the dough temperature thanks to the water content (sensible heat), and then, when reaches that temperature, serves to evaporate the water from the dough (latent heat). This process is time and temperature dependent, and generates a temperature increase process in a first baking stage and a water evaporation process in a second baking stage (M. Ureta et al., 2019).

Research Question

Pizza is one of the most consumed bakery products around the globe (Liu et al., 2020). There is a lack of information regarding the effects of different technologies on the end quality of the pizzas, other than nutritive loss of pizza crust attributed to heat and influence of ingredients (Glicerina, Balestra, Capozzi, Dalla Rosa, & Romani, 2018; Tsen, Bates, Wall Sr, & Gehrke, 1982). Machine vision system and image analysis techniques had been used for quantification of color and uniformity of different cheeses in pizza, influenced by transition temperature (Ma, Balaban, Zhang, Emanuelsson Patterson, & James, 2014). Few studies revealed that in many of the modern ovens, radiative heat transfer is the very predominant and most efficient mechanism for pizza baking, except for steam injection, and that the surface pizza emissivity has an effect on the temperature-time evolution (Nicolas, Glouannec, Ploteau, Salagnac, & Jury, 2017; Soleimani Pour-Damanab et al., 2014).

There is a need for faster cooking methods that produce uniform cooked pizzas and conserve its cooking and sensory quality. This work answers the need of the company Welbilt Inc. of reducing the cooking time by half, redesigning a conveyor belt oven incorporating new technologies.

General Objective

The general objective of this work is to redesign a conveyor oven to reduce the cooking time by using alternative cooking technologies.

Specific Objectives

The specific objectives proposed are:

To define a methodology that allows us to evaluate instrumentally the pizza quality.

To study the effect of different heating technologies on the cooking and quality properties of pizza baked in commercial ovens, pursuing the reduction of baking time.

To evaluate the energy and CO2 emissions given a reduction in the baking time.

Structure of the thesis

This work will be divided into six sections to break down the process followed step by step:

Chapter 1. Introduction

Chapter 2. Metrics to evaluate the cooking performance and energy consumption.

Presents the methodology used to evaluate the cooking performance, define the objective characteristics of pizza, and the cooking energy consumption.

Chapter 3. Baseline definition and validation: Describes the pizza of reference or baseline pizza to compare the changes in the pizza baked with different technologies.

Chapter 4. Technology identification: Details the methodology through which the technologies were selected.

Chapter 5. Technology evaluation: Results of the effects of cooking technology in the final pizza s characteristics.

Chapter 6. Conclusions: Gather the conclusions of the work explained in the previous chapters and the recommendations for the improvement and optimization of the prototypes.

Chapter 2

Metrics and methods to evaluate the pizza quality

Aiming to establish the set of instrumental tests that describe pizza quality and acceptability, well- accepted pizzas were cooked using different oven technologies starting from the same type of dough.

Then, a set of instrumental methods to measure their physic-mechanical characteristics were proposed. Pizza characteristics were grouped in cooking quality parameters and sensory parameters.

The quality parameters were measured by Rapid Visco Analyzer (RVA) and Scanning Electron Microscope (SEM), while the sensory qualities were described by final temperature, density, moisture content, weight loss, color, and texture. Finally, a sensory analysis was conducted with 60 non-trained panelists to validate that they could not identify their differences, as long as pizzas exhibit similar values for the proposed metrics. Next, we will describe each of these steps.

2.1 Pizza preparation

The dough was prepared following the method proposed by (Larsen, Setser, & Faubion, 1993), but modified with some recommendations of a local pizza store. The following ingredients and proportions were used (flour weight basis) to prepare 9 in diameter, 240g pizza breads: 1200g (100%) of wheat flour with 13% protein (Hoja de Plata, Harinas Elizondo, Mexico), 780g of tap water (65 %), 12.96 g of salt (1.08 %) and 12.96 g of sugar (1.08 %) (HEB, Mexico), 4.32 g of instant dry yeast (0.36%) (Safmex, Mexico) and 36g of edible vegetable oil (3%) (Aceites grasas y derivados, Mexico).

The dried ingredients were mixed in an A200-T, Hobbart Mfg mixer (Troy, OH) at speed level 1 for 1 min. Subsequently, the oil was added and mixed for 1 min. Finally, the water was added at 25°C and mixed for 2 min. The dough mixture was kneaded for 7 min at level 2 and for 3 min at level 3.

The dough was weighed and portioned within 30 min after kneading, and the portions were placed in lightly greased aluminum trays. Then they were covered with plastic film and refrigerated (4° C) for 24 hours. Before cooking, the trays were removed from the refrigerator and left outdoors for a maximum of 30 min. The dough was spread with a wooden roller and placed in aluminum trays. 64 g of pizza sauce (Del Fuerte, Mexico) and 100 g of grated Manchego cheese (FUD, Sigma, Mexico) were added as toppings.

2.2 Pizza characterization

Physic characteristics

Pizzas were subjected to the next measurements as soon as they leave the oven: final temperature, weight loss, specific volume, and water content. Pizza s temperature was monitored with a 0.25 mm diameter K-type thermocouple (SRTC-GG-K-30-36 Omega, San Pedro 120 Garza García, Mexico).

The final temperature of the pizzas was measured at five random points separated 2in from the edge of the pizza and one in the center. The reported value corresponds to the highest temperature taken.

A thermocouple reader (Krypton 16xTH, Dewesoft, Mexico City, Mexico) and a data logger (SBOX, Dewesoft, Mexico City, 122 Mexico) were used to collecting the data. The visualization of the data was done through the DAQ Dewesoft X3 software.

The weight loss was computed using Equation 1.

𝑊𝐿 𝑊

𝑊 𝑊 𝑊

𝑊

Where, Wd,i is the initial weight of the dough, Wf is the final weight of the pizza, Ws is the weight of the sauce and Wch the weight of the cheese that was added in the preparation of the pizza. Pizza diameter and thickness were measured with a Vernier. Equation 2 was used to compute the volume of the pizza and its specific volume. Dp is the diameter of the pizza, and hp is the average height of the pizza.

𝑆𝑉  𝐷  ℎ

4 𝑊 𝑊 𝑊

The final water content of the pizza was measured as follows: three random portions of the pizza were taken, weighed, frozen overnight and freeze dried (Labconco FreeZone 6 plus, Kansas City, MO, USA) at a pressure of 0.024 mBar and temperature of -84°C for 24 hours. In the case of pizzas with toppings, the sauce and cheese were previously removed. Finally, the lyophilized samples were weighed, and Equation 3 was used to compute the final moisture. Where, Wi is the initial weight of the sample, Wf is the final weight of each lyophilized sample.

𝑀𝐶    𝑊    𝑊

𝑊 100%

Cooking quality characteristics

Cooking quality was measured using rapid viscosity analysis (RVA) and SEM microscopy. Initially, the pasting properties of the samples were determined using a RVA4500 (Perten, Sweden). The sample was obtained by grinding the freeze-dried crust with a Cyclone Sample Mill (Model 3010-03, UDY, USA). A 13% solids suspension was agitated continuously at 160 RPM. The heating profile was: keeping the temperature at 50° C for 1 min, heat to 90 °C at 15 °C/min, keeping the temperature at 90 °C for 4 min, cool to 50 °C at -15 °C/min, and keeping at 50° C for 2 min., the STD1 profile was used in this test, based on AACC Method 76-21.01, ICC Standard No. 162. Data was obtained through the Thermocline for Windows version 11.2 software (Perten, Sweden).

The structural properties of the crumb were observed in an EVO MA 25 SEM (Zeiss, Germany) as in (Wu, Chen, Li, & Wang, 2010). Briefly, freeze-dried samples of pizza crust of 1 cm x 1 cm, were mounted on aluminum stubs using double-sided tape. The samples were coated with 10nm of gold film, examined under high vacuum conditions at an accelerating voltage of 10 kV.

Quality parameters

The textural characteristics of the pizza were measured with the TA.TX Plus texture analyzer (Stable Micro Systems, Godalming, UK) in two modes: extensibility and compression. For the extensibility modality, pieces of 15 cm x 3 cm were cut and placed in the attachment points of the Pizza Tensile Rig attachment (A/PT 5 kg). The tensile strength of the sample was measured, the test speed was set

at 5.0 mm/s, and a travel distance of 45 mm. For compression mode, 5 cm x 5 cm pieces were cut and placed on a sample holder. A double compression test was carried out at 40% of the workpiece height, using a 21 mm diameter aluminum cylindrical probe. The speed of the test was set at 1.7 mm/s. For both cases, the Exponent software version 6.1.12.0 (Stable Micro Systems, Godalming, UK) was used. Firmness, cohesiveness, chewiness, resilience and toughness data were obtained from this test.

For measuring color, the samples were placed in a professional photography set (Puluz PU5040, place), which consisted of two LED lights of 30 W each and an intensity of 5500 lumens. Photographs of the top, bottom and cross section of each pizza were taken. Figure 1 show the arrangement to obtain the images.

Figure 1. Photographic set configuration to evaluate BI and BF. The camera and the PC are not connected in real time, after taking the photos, they were copied via SD memory to the PC.

The images were processed by computer using ImageJ software (version 1.51 (100), National Institutes of Mental Health, Bethesda, Md, USA) and the values of color characteristics were obtained in the CIE L*a*b space for pizzas without toppings. The CIE L∗a∗b model is an international standard for color measurement developed by the Commi ion In e na ionale de l Eclai age (Yam &

Papadakis, 2004). The three parameters of such model represent the lightness of color (L∗) which ranges from 0 to 100 (black to white), its position between red and green (a∗, values between 120 and +120) and its position between yellow and blue (b∗, values between 120 and +120). Since images were obtained in the RGB color space, an algorithm developed in MATLAB 6.5 (The MathWorks, Inc., Natick, MA, USA) was used to obtain CIE L∗a∗b∗ values, as in (Purlis &

Salvadori, 2007) and to compute the color parameters. Changes in the crust color were estimated by the Browning Index (BI) (Kurek, Wyrwisz, Piwi ska, & Wierzbicka, 2016) (Equations 4 and 5):

𝑎 1.75𝐿

5.645𝐿 𝑎 3.01𝑏

𝐵𝐼 100 0.31

0.17

Changes in cheese color were estimated by a Browning Factor (BF) (Wang & Sun, 2003). This factor is defined (Equation 6) as the ratio between the average gray value of the cheese image (Gray Value

- GV) before cooking and after cooking. GV has a range between 0 and 255, where 0 corresponds to totally black and 255 to totally white.

𝐵𝐹 𝐺𝑉

𝐺𝑉 100%

Where, GVi is the initial GV of the cheese before cooking and GVf the final GV after cooking. These color measurements were calibrated using a blank, white surface.

Sensory analysis

The sensory analysis of the pizza was carried out with a non-trained voluntary panel of 60 people, consisting of 42 men and 18 women in the ages of 18 to 37 years. The properties of the pizzas were evaluated with a hedonic test in which a value of 1 to 10 was given to the smell, taste, color, texture and overall appearance of the topping pizza (Cooper, Catauro, & Perchonok, 2012). Each pizza was cut into 12 triangular portions, which included crust. Each panelist was asked to evaluate the color and smell before trying it. Right after, he was asked to chew it to evaluate taste and texture. Finally, he was asked to assign a rating to general acceptance (Bernklau et al., 2017).

Statistical data analysis

All measurements were carried out with a minimum of 3 replicates per sample. Measurements are presented as their average and standard deviation. An analysis of variance (ANOVA) was used to determine if there were statistical differences among the values measured for each property of the pizzas with a 95% confidence interval. MINITAB 18.0 software was used to compute the significant differences by applying the Tukey method for a p-value <0.05.

Chapter 3

Determination of the values for the metrics that define a well acceptable pizza

A set of experiments was carried out in order to test the effectiveness of the methodology established in the previous chapter and to stablish reference values for a well acceptable pizza. Three batch ovens with different cooking methods were used to bake pizzas. Then, the characteristics of those pizzas were obtained, and a sensory analysis was carried out. Table 1 shows the features of the ovens used in this study. These ovens were located at the Biotechnology Center FEMSA of Tecnologico de Monterrey (Monterrey, Mexico).

Table 1. Characteristics of the ovens used to cook pizzas. The fan velocity was set as default in each oven. Only Tech3 oven has a tray turning mechanism.

Oven Power supply (VAC/Phases/Hz/kW)

Gas thermal power (kW)

Cooking time (min)

Fan velocity

Chamber dimensions

(cm)

Cooking method

Tech1 120/1/60/0.3 16.5 7 High 54 x 44 x 71 Combustion

products convection

Tech2 220/3/60/10.1 - 5.5 5 56 x 70 x 49 Hot air

convection and radiation

Tech3 220/3/60/1.5 23.26 12 Low 94 x 99

x 101.6

Combustion products convection

The cooking temperature was kept constant at 240° C in all equipment. There was only one fixed fan speed in ovens Tech1 and Tech3. In the case of the oven Tech2, the equipment operation manual was reviewed to determine the fan speed level appropriate for pizzas.

During the cooking process, we followed the instructions of a pizza expert. Those recommendations translated into the establishment of the cooking time. After baking several pizzas, we observed that a Browning Index (BI) of 60 was necessary to ensure the correct cooking of the pizza. That values agree with the values reported in other works (Galvão, Zambelli, Araújo, & Bastos, 2018; Sakin- yilmazer et al., 2013; Wronkowska, Haros, & Soral- mietana, 2013). Therefore, we defined the cooking time as the time to reach a greater than 60 in the pizza crust.

Pizzas were baked three times per oven position (Figure 2, Table 2). In the Tech 1 oven, a tray with two pizzas was introduced, one at each side, and after that, a tray with one pizza in the center was introduced. In the Tech2 and Tech3 ovens, a tray with three pizzas was introduced, with the only difference that the Tech3 oven has a tray turning mechanism. The trays were of the following dimensions: 50 cm x 35 cm for the Tech1 oven and 65 cm x 45 cm for the Tech2 and Tech3 oven.

Figure 2. Position of the pizza trays inside the ovens. L, C and R Stands for Left, Center and Right for the Tech 2 and Tech 3 ovens. In the case of Tech1 the positions are A, B and A for left, right

and center, respectively. The scheme of the thermocouples that were placed inside the ovens to measure temperature during cooking, is shown.

Table 2. Coordinates for each point inside the Figure 2. The coordinates points are (x, y, z).

Position Tech 1 Tech 2 Tech 3

1 (280, 195, 395) (360, 210, 130) (80, 740, 150)

2 (260, 205, 600) (370, 300, 490) (80, 740, 890)

3 (280, 210, 90) (40, 80, 110) (860, 740, 210)

4 (290, 380, 400) (240, 240, 290) (860, 740, 700)

5 (90, 370, 95) (670, 240, 490) (440, 0, 0)

6 (55, 65, 605) (470, 240, 290) (440, 90, 1010)

7 (375, 30, 75 (50, 390, 460) -

8 (500, 550, 610) (670, 420, 80) -

9 - (320, 170, 500) -

Six to nine, K-type thermocouples of 0.25mm in diameter (SRTC-GG-K-30-36 Omega, San Pedro Garza García, Mexico) were distributed in the baking chamber of each oven. A thermocouple reader (Krypton 16xTH, Dewesoft, Mexico) and a data logger (SBOX, Dewesoft, Mexico) were used. The visualization of the data was done through the DAQ Dewesoft X3 software.

3.1 Results of the methodology to obtain a well acceptable pizza

First, the cooking time for each oven technology was obtained as the minimum time for getting a Browning Index (BI) of 60. For measuring BI, pictures of the pizza were taken in the same manner (same light level, visual angle, and camera). Thus, none of these factors could affect the comparison among images. In Figure 3, a set of selected photographs are shown. The last picture of the sequences

x z

y

990 940

1016

2

1

3 4

5 6

508

1 7

6 5 4

3

2

8 9

700

560

490

250

x z

y

1 2

3 4

5 6

7

8

711

533

584

250

x z

y

Tech 1 Tech 2 Tech 3

for each oven technology corresponds to the pizza that was baked with the right cooking time. All of the remaining experiments for each oven technology were performed using this selected time

.

Figure 3. Image sequences of pizzas cooked at different times in the three ovens, in order to select the minimum cooking time, indicating their computed Browning Index.

Mea emen of i a cooking ali parameters

For bakery products, two cooking times can be defined. The first is related to the complete transition from dough to bread. That cooking time ensures the acceptability of the product (Purlis & Salvadori, 2007). The second time (end time or quality time) will be discussed later.

The dough to bread transition was confirmed by measuring the degree of starch gelatinization on the resuspended crust. Gelatinization involves the swelling, exudation, and rupture of the granule. It implies changes in the viscosity of starch dispersions when the heat is applied. When studying the pasting behavior of starch from corn dried at different temperatures, the peak viscosity decreased when the drying temperature was higher. At higher drying temperatures, the swelling capacity of starch decreases. Likewise, there is a tendency to increase in the final viscosity of dried corn by increasing the drying temperature (Malumba, Massaux, Deroanne, Masimango, & Béra, 2009). All of the above is attributed to the stiffness of the grain and lipid content, among others.

Figure 4 shows the results of the RVA tests carried out for pizza. This test was performed to corroborate the gelatinization of the starch and verify whether it was total or partial. For comparison purposes, Figure 5 shows the visco-amylograms obtained for the raw flour used to prepare the pizzas, for the fermented and the unfermented dough. As observed, no gelatinized starch (flour) shows a higher viscosity peak than the rest of the formulations. In the case of the fermented dough, yeast amylase partially hydrolysates starch, so a sharp drop in viscosity can be observed. Compared to

these, the RVA results for pizzas showed lower peak and final viscosities. The shapes of the visco- amylograms (Figure 4) served to distinguish the cooking level of the finished product. In Figure 5, the peak in viscosity cannot be easily observed, indicating that, in effect, the complete cooking of the product was achieved (Crosbie & Ross, 2007).

The arrangement of pizzas inside the ovens was different. There was more data dispersion for the ovens in which the pizzas were cooked in two trays compared to the data for the oven equipped with a rotary table (Table 3). This observation was expected since the rotary table provides the pizzas with uniform heat distribution.

a b

c

Figure 4. Sequences of RVA viscoamylograms of bread cooked in the three different ovens (a).

Viscoamylograms of pizza samples cooked at three different positions in the Tech1 (b) and Tech2 (c) ovens, respectively. The horizontal gray line is added for reference only. The vertical lines that

produce the shading are the error bars.

Figure 5. Pasting profile of flour and dough (fermented and unfermented).

SEM analysis can be used to measure cooking quality as well. SEM is one of the most used instrumentation to observe differences between different types of baking. The uncooked crust can be seen in SEM micrographs as round starch granules of raw flour (Blutinger et al., 2018; Martínez- Bustos et al., 1999). (Sánchez-Pardo, Ortiz-Moreno, Mora-Escobedo, Chanona-Pérez, & Necoechea- Mondragón, 2008) carried out a study with SEM microscopy to determine the differences between bread cooked with conventional technology and microwave technology. When bread doughs were cooked with microwave, the crust was not fully cooked, and the starch granules appeared almost unaltered, while for hot air baked bread, a uniform gluten matrix was observed.

Table 3. RVA values of the pizza samples baked in three different ovens.

Tech1 Tech2 Tech3

Peak viscosity (cP) 476.0 ± 73.64 a 462.7 ± 34.52 ab 399.2 ± 18.69 b Trough viscosity (cP) 454.6 ± 67.20 a 433.0 ± 44.45 ab 380.2 ± 17.44 b Final viscosity (cP) 671.2 ± 67.09 a 643.3 ± 67.44 a 554.8 ± 29.97 b Breakdown (cP) 21.4 ± 7.30 a 29.7 ± 17.74 a 19.0 ± 4.90 a Total setback (cP) 216.6 ± 16.86 a 210.3 ± 34.32 ab 174.7 ± 29.44 b Peak time (min) 6.6 ± 0.10 a 6.8 ± 0.19 a 6.6 ± 0.14 a Pasting temperature (°C) 94.5 ± 0.26 a 89.5 ± 12.35 a 94.6 ± 0.22 a

Our results of cooking quality were corroborated via SEM micrographs analysis. Micrographs with 700x magnifications of the pizza powder used for RVA are shown in Figure 6, in which the structural characteristic of the crumbs seem similar regarding their size, appearance, and protein matrix structure. However, small differences can be observed in Figure 6 that are related to the RVA results.

The viscosity peaks for Tech1 and Tech2 cooked samples are similar, and their correspondent micrographs are also similar. However, an attentive examination of these figures shows that these crumbs have some small granules on the surface. This characteristic is enhanced in the photograph of Tech 2 samples. On the contrary, for Tech 3 samples, which were cooked uniformly, their micrographs show bigger, smoother pieces and without any granules on the surface. It has been demonstrated before that for short baking times, the swelling of the starch granules is partial, while

for longer cooking times, a gel-like structure is observed, and no ghost of starch granules can be identified (Aissa, Monteau, Perronnet, Roelens, & Le Bail, 2010). Also, discontinuities between starch granules and gluten matrix have been related to crumbs with weak points, providing a source for a more failure mechanism when subjected to low strains (Stokes, 2000), yielding smaller crumbs.

This observation can also be explained at a chemical level. For longer baking times, the leached amylose from the starch granules increases. It acts as a cement that stabilizes the matrix and stands the strain caused by the gluten (protein) network, which contracts during the subsequent freezing- dehydration cycle at which the samples are exposed (Aissa et al., 2010).

a b

c

Figure 6. Micrographs of the bread crumbs used in the RVA tests. The micrographs were taken under 700X magnification, 10kV, High Vacuum Mode. a) Tech1. b) Tech2. c) Tech3.

Mea emen of i a en o ali parameters

RVA and SEM results confirmed that the pizzas were well cooked, satisfying the minimum acceptability criteria of the product. That is the complete transition from dough to bread and was confirmed. The second cooking time (the end time or quality time) is associated with consumer preference, and it is related to the sensory parameters of the product. Next, we will describe the results of the quality parameters of the produced pizzas.

Measurements were made on the following physical properties of the pizzas: temperature, weight, dimensions, as well as water content. Subsequently, pizzas were subjected to color measurements and afterward to texture measurements (compression and extensibility). These tests were made for pizza with toppings and without toppings. Table 4 shows the result of the measurements of the pizzas properties immediately after baking. The final average temperatures of the pizzas with toppings were similar for the three ovens (p > 0.05), around 99°C. As expected, it was observed that the longer the cooking time, the lower the water content of the pizzas.

Table 4. Physical characteristics of pizza cooked in 3 different ovens.

Oven Tech1 Tech2 Tech3

With Toppings

Temperature (°C) 99 ± 0.82 ab 100 ± 0.93 a 98 ± 1.09 b

Spec. volume (cm³g^-1) 3.6 ± 0.17 a 2.7 ± 0.23 b 3.0 ± 0.10 b

Weight loss (%) 16.8 ± 0.92 a 12.3 ± 1.14 b 19.0 ± 2.70 a

Moisture (%) 40.3 ± 2.83 ab 41.0 ± 1.38 a 36.9 ± 1.40 b

Without toppings

Temperature (°C) 103 ± 1.40 a 104 ± 0.00 a 98 ± 1.49 b

Spec. volume (cm3g-1) 2.8 ± 0.24 a 2.6 ± 0.19 a 2.7 ± 0.13 a

Weight loss (%) 13.6 ± 0.80 a 13.5 ± 0.70 a 13.9 ± 0.75 a

Moisture (%) 32.1 ± 1.23 a 31.7 ± 1.05 a 29.3 ± 0.70 b

Color*

Browning Index 64.3 ± 7.13a 61.8 ± 13.37a 57.9 ± 4.84a

Browning Factor 167.0 ± 2.36a 163.6 ± 3.06a 150.8 ± 6.27b

Texture

Without toppings

Firmness (g) 1073 ± 156 a 2703 ± 587 b 1378 ± 179 a

Cohesiveness (%) 0.84 ± 0.01 a 0.74 ± 0.02 b 0.79 ± 0.01 c

Chewiness (g) 375 ± 41 a 1832 ± 324 b 1058 ± 110 c

Resilience (%) 0.37 ± 0.01 a 0.31 ± 0.02 b 0.36 ± 0.02 a

Toughness (g-sec)** 2028 ± 102 a 1861 ± 115 b 2071 ± 109 a

*Color was also compared within ovens (position), and no statistical difference was found.

Values followed by the same letter (a, b, c) are not significantly different at 95% confidence interval.

**With toppings.

In the case of pizzas without toppings, there was a difference of 6°C between the final temperature of pizzas produced in Tech1 and Tech2 ovens with respect to the Tech3 oven (p < 0.05). Unlike the previous case, the lack of toppings gave way to the rapid formation of the crust. Table 4 shows that the final temperature of the pizzas for the Tech3 oven does not exceed 100° C, although it was the oven that had the longest cooking time. We observed that the temperature inside the cavity in the Tech3 oven was not uniform (Figure 7). Furthermore, it had a lower airspeed. All these reasons may have caused a more moderate water content for the pizza baked in this oven, which is also related to the values of peak viscosity and final viscosity of the samples, compared with those produced by the other two ovens.

Table 4 also shows the textural characteristics of the pizza obtained by compression and by extensibility test. The compressibility test consists of compressing a piece of the product to be evaluated with a cylinder, at a certain distance, and predefined force, resembling the bite of a human being. The extensibility test, by contrast, uses two aluminum plates on which the sample is secured.

The plates are separated until the food breaks, while the force applied for the extension is recorded.

The compression test was carried out only for pizza without toppings. The extensibility test for pizza with toppings.

Figure 7. Temperature profiles inside the oven cavity, during cooking. a)Tech1, b)Tech2, c)Tech3.

In the case of the texture of pizza without toppings, it can be seen that pizza cooked in Tech2 oven had a greater firmness, although it had the shortest cooking time, which indicates a high heating rate.

This observation has been related to higher moisture content (due to a higher heating rate and rapid formation of the crust), as well as a higher peak viscosity and final viscosity of the analyzed samples (Patel, Waniska, & Seetharaman, 2005). An early formation of the crust makes water transfer difficult, which causes a crumb with more water content. It also limits the expansion of the produced bread, generating a denser structure, that causes bread hardening (Zhang, Lucas, Doursat, Flick, &

Wagner, 2007). The chewiness is related to hardness and in the case of cohesiveness (bread structure strength) and resilience (a degree on which the bread returns to its original shape). They are related to a high specific volume (Soleimanifard, Shahedi, Emam-Djomeh, & Askari, 2018). Tech1 oven temperature was uniform and produced pieces of bread with a higher density. Regarding the pizzas with toppings, the values of toughness observed indicate that less energy is needed to break the samples produced by the Tech2 oven, compared with Tech1 and tech3. A cohesive bread structure provides high values of resistance to extension and toughness (Glicerina et al., 2018).

There is a relationship between the heating rate and the quality parameters of the pizzas. In most of the tests that were carried out, the pizzas produced by the ovens Tech2 and Tech3 were statistically different (p < 0.05), which indicates that for different heating methods, the quality and texture properties will be different (Patel et al., 2005). The same behavior is seen in the quality of the starch present in the cooked samples. Although the visco-amylograms indicate that complete cooking was obtained, the final viscosity values were different. With these results, we could see that the instrumental techniques used to characterize the pizzas cooked with three separate ovens, serves to find differences in their characteristics.

Sensory evaluation

So far, we have ensured the quality of the pizza with objective values of sensory parameters (color and texture) associated with the consumer's preference (Purlis, 2019). However, the quality of the pizza should be validated through the consumer´s acceptability. Therefore, this study was complemented with a sensory analysis, using non-trained panelists.

The analysis of the answers of the non-trained panelists revealed that there was no significant difference (p > 0.05) in the results obtained (Figure 8), with the exception of color, in which the Tech2 oven pizza had a better color score (p < 0.05), than the Tech1 oven pizza. For all other features, the Tech2 oven pizza had a higher average rating than the other ovens. Although the instrumental tests of texture and color showed differences between the ovens (especially the Tech2 oven), the people who participated in the sensory tests, which are frequent consumers of pizza, were not able to identify those differences. This shows the sensitivity of the instrumental methodology for the evaluation of the physical and quality properties of the pizzas that were proposed in this work.

Figure 8. Average of sensory analysis values. Only Color is significantly different (p-value = 0.022).

From the previous observations, the following assertions could be made. First, the final temperatures were similar for the case of pizzas with toppings. However, the temperatures that reached the pizzas depended on the cooking time, the airspeed, and the arrangement of the pizzas inside the oven. These

circumstances gave each pizza (with and without toppings) different physicochemical and sensory characteristics, which were evaluated by instrumental tests and by an untrained sensory evaluation panel. The level of cooking of the pizzas was complete in all cases, ensuring the gelatinization of the starch and producing an acceptable product. On the other hand, the quality characteristics of the pizzas varied depending on the oven technology in which they were baked. Even though the instrumental evaluation shows some differences in the pizza quality characteristics, the non-trained sensory panel did not identify those differences. Thus, it can be affirmed that the instrumental assessment used in this study can largely replace the consumer evaluation, at least for the region in which the pizzas were prepared. In Table 5, we present a summary of the instrumental methods used and their typical values for an acceptable pizza in northern Mexico.

Table 5. Method to evaluate pizza quality and representative values of acceptable simple pizzas in northern Mexico, after baking. In this range, untrained panelists did not find differences. All data is

presented in terms of mean ± 95% confidence interval.

Parameters Property Test/Instrument Representative value Cooking

quality

Starch gelatinization RVA (13% solids) Peak viscosity: 446.0 ± 31.5 cP Final viscosity: 623.1 ± 37.62 cP

Crumb structure SEM Uniform, no round starchy crumbs observed Pizza with toppings

Sensory quality

Final temperature Thermocouples 99 ± 0.44 °C Specific volume Gravimetry/Vernier 3.1 ± 0.08 cm³/g Moisture content Freeze-drying 35.9 ± 7.3 %

Weight loss Gravimetry 16.03 ± 0.82 %

Color Browning index 61.33 ± 4.24

Browning factor 160.27 ± 1.96

Texture Extensibility Toughness: 1986 ± 50.25 Pizza without toppings (pizza crust)

Sensory

quality Final temperature Thermocouples 101.3 ± 0.55 °C Specific volume Gravimetry/Vernier 2.7 ± 0.09 cm³/g Moisture content Freeze-drying 31.03 ± 0.47 %

Weight loss Gravimetry 13.67 ± 0.35 %

Color Browning index 61.33 ± 4.24

Browning factor 160.27 ± 1.96

Texture Compression

Firmness: 1718 ± 168.89 g Cohesiveness: 0.79 ± 0.01 % Chewiness: 1088 ± 91.91 g Resilience: 0.35 ± 0.01 %

It is important to highlight that even when the cooking method was different (i.e. combustion products convection vs. hot, forced air convection), the panelists did not find significant differences. Thus, we suggest using the set of analytical tests developed in the present study for the evaluation of any oven technologies (gas, infrared, or steam, for example), instead of a sensory analysis, using non-trained panelists. This alternative will expedite the process of innovation on pizza formulation, pizza baking process, and oven technology. This process for pizza characterization will be used in the next chapters.

Chapter 4

Technology identification

A commercial impingement oven was used as a baseline or reference against which to compare the integration of other technologies for the experiments. This oven technology is widely used in the restaurant industry. The dimensions and specifications of the equipment are presented in Table 6. The oven temperature is regulated by a build-in J type thermocouple that controls the electric heating resistor on and off.

Table 6. Characteristics of the conveyorized electric oven Digital Countertop Impinger (DCTI) used in this study.

Cavity (LxWxH) Input rate Temperature range

Cook time range

Technology Airspeed 0.52 m x 0.43 m x

0.19 m 6 kW 93°C to 316°C 30 sec to 15

min Air

impingement Fixed

Various tests were carried out to determine the temperature and cooking time of the pizzas that would serve as a reference. Again the browning index (BI) was selected as an evaluation criterion for the quality of the pizzas. Reported BI values for bakery products are between 51 and 60 (Galvão et al., 2018; Sakin-Yilmazer et al., 2013; Wronkowska et al., 2013). We chose a value of 60 for the reference sample. First we combinined different temperatures and cooking times, using only air impingement technology. In Figure 9, a representative diagram of the temperature versus cooking time combinations is shown. The obtained BI for each pizza is appreciated. The selected reference pizza was the one baked at a temperature of 260 C for 6 min: 30 sec, as it s BI is around 60.

Figure 9. Image sequences of pizzas baked at different times and temperatures with only HA, in order to select the cooking time and temperature of the reference pizza, indicating their computed

Browning Index.

12.75

81.67 47.57

60.47 53.16

50.80

27.39 22.95

72.67

Speed belt (min)

Air Temperature (°C)

315

260 287

5.5 5 6.5

Once the time and temperature were selected for the reference pizza, we monitored the temperature increase of the dough during the baking process. During the path of the pizza through the oven, four thermocouples carefully located inside the dough were measuring the temperature increase. Figure 10 shows the obtained temperature profile. From these measurements three cooking stages could be identified (Patel et al., 2005). Stage 1 with pizza temperature between 25 °C and 65 °C which ends with the start of the starch gelatinization process (Nicolas et al., 2017; Therdthai, Zhou, & Adamczak, 2002), Stage 2 with pizza temperature between 65 °C and 100 °C, and ends with the start of water evaporation, and Stage 3 with pizza temperature above 100 °C, which ends when the BI of the pizza is about 60. There is a phenomenon of moisture diffusion in stage 2, from the center to the surfaces due to the increase in temperature and internal pressure inside the pizza given by the evaporation of water (Ploteau et al., 2015). The surface of the pizza when losing moisture allows caramelization reactions that give way to the formation and browning of the crust (Vanin, Lucas, & Trystram, 2009).

This phenomenon is predominant in Stage 3. Due to the structure of the crust, a decrease in moisture loss and flattening in the internal temperature occurs, decreasing the heat and moisture transfer rate.

The temperature profile shown in Figure 10 will serve to identify the application of each technology in later sections.

Figure 10. Temperature profile of the reference pizza (260 °C, 6 min: 30 sec).

4.1 Technology selection and experimental setup

A set of technologies for cooking food was selected considering their aplication in the food processing industry and the changes in the physical properties of the food. After that, it was obtained a list of technologies that are used for the thermal and non-thermal treatment of food. A decision matrix is a methodology that allows us to deal with lists like this one, where we have an extensive number of options, and only a few must be chosen. These matrices work by evaluating a series of criteria with each of the possibilities and assigning a rank to select the one that performs best regarding those criteria. Table 7 show the positive negative decision matrix that we used in this work, in which a baseline technology was chosen to compare the others. For this, the baseline was a standard convection oven. The criteria evaluated in the decision matrix were carefully selected, seeking to reduce the number of technologies to the minimum possible but which had the most significant impact on cooking time, food quality, degree of innovation, and in the emissions of pollutants associated with the use of this technology. From these tables, each of the options is assigned a cross (+) if it is better compared to the baseline, a zero (0) if it does not show improvement, and a minus (-) if it is counterproductive to use it. Then the number of minus signs is subtracted from the number of pulses for each of the technologies, and a value is assigned depending on the range in which it has been with

0 20 40 60 80 100 120

0 50 100 150 200 250 300 350 400

Temperature [°C]

Time (s) Stage 2

Stage 1 Stage 3

that difference (see Figure 11). Finally, the technologies selected were: microwaves, infrared, magnetic induction, and superheated steam. In Figure 12, is showed the experimental setup used for each of the techonoloy selected and their suitable combinations.

Figure 11. The selection ranges for the decision matrix.

Microwave

Microwave heating is the most popular and the most extensively studied emerging technology in food processing worldwide in domestic and industrial applications due to its advantages in heating rates, reduced processing time, smooth operation, lower maintenance requirements, and reduced use of plant space (Tang, 2015). The use of microwaves for baking products in combination with other technologies has been studied and a reduction in cooking time of up to 75% has been achieved (Sakiyan et al., 2011; Gülüm Sumnu, Datta, Sahin, Keskin, & Rakesh, 2007; Gulum Sumnu, Sahin,

& Sevimli, 2005).

Figure 12. Diagram of the implemented technologies combination. a) Microwave and Air Impingement, b) Superheated Steam and Air Impingement, c) Induction, Infrared and Air

Impingement, and d) Infrared and Air Impingement.

2 1

3 4

10 – 13

5

7 – 9 4 – 6 1 – 3 -13 – 5

Rank Net

Table 7. Positive-negative matrix for the selection of potential technologies (+, better; 0, same; -, worse).

Selection criteria Induction Infrared Microwaves Radiofrequency Superheated steam Ultrasound Laser Ohmic heating Hot air

impingement Convection

Resulting oven size + + + - + + - + + 0

Resulting oven cost - + + - + - - + + 0

Energy consumption - + + - + + - + + 0

Easy of use + + + - + - - + + 0

Cost to customer + + + - + - - + + 0

Marketability + + + + + + + + + 0

Potential to reduce

cooking time + + + + + + - - 0 0

Emission reduction + + + - + + - - 0 0

Ease implementation + + - - + - - - + 0

Safety - 0 - - + + - - + 0

Maintenance + + + - - - - 0 + 0

Food

quality preservation + + + 0 + 0 0 0 + 0

Degree of innovation + - - + - + + + - 0

Pluses 10 11 10 3 11 7 2 7 10 0

Sames 0 1 0 1 0 1 1 2 2 13

Minuses 3 1 3 9 2 5 10 4 1 0

Net 7 10 7 -6 9 2 -8 3 9 0

Rank 2 1 2 5 1 4 5 4 2 5

Continue? Yes Yes Yes No Yes No No No Yes No

Figure 12a shows the configuration of the combination of Microwave and Hot Air Impingement technologies. Based on the cooking stages showed in Figure 10 it was decided use the microwaves to heat up the pizza in the stage 1 in order to increase rapidly the internal temperature of the pizza due to its high heating rate. Later, Hor Air Impingement conveyor oven is used to finish cooking stages 2 and 3. Using Microwaves in stages 2 or 3 was difficult due to the installation into the conveyor oven.

The cooking time starts when the pizza is introduced into the microwave oven and end when the pizza start to leave the conveyor oven. A domestic microwave was used in these experiments (Kor-660s 600W, Daewoo, Monterrey, Mexico) and then the pizza was introduced into the oven with hot air impingement. In the microwave, the pizza was placed on a plastic surface that allowed it to be removed and then passed to a commercial aluminum tray with holes on which it was moved into the air impingement oven. Varied factors were: The power in a range of 300W to 600W and the residence time from 10s to 6min 30s. Air impingement was kept at a constant temperature of 287°C in a range of 3min to 4min 20s. The parameters for this combination of technologies that produce a pizza of acceptable quality will be discussed later.

Superheated steam

A benefit of the use of superheated steam as a cooking method is the potential reduction in cooking time. Its high energy content and the condensation of water on the food surface allow a rapid increase in the internal food temperature due to the high heating rate and moisture loss rate (Araki et al., 2018).

Its effects on bakery products and food drying have been studied; however, there is little information on applications where it is part of pizza baking. It is hypothesized that by applying superheated steam to the first stage of cooking, the pizza temperature increase will be pronounced, and therefore the time in this stage will be decreased.

Figure 12b also shows the schematic of the superheated steam application. A 0.25-inch copper tube with a 4.2m length bent into a coil shape and with the end hermetically sealed was used inside the oven cavity. Fifty-one 0.0625-inch holes equally spaced 1in were made in the last four sections of the tube. Water is passed through the tubing, followed by 20 mL of air using a syringe pump (Chemyx Fusion 100, Chemyx, TX, USA). A 20 mL syringe was used. In the experimentation, volumes of water from 4 mL to 10 mL, and injection rates from 8 mL/min to 32 mL/min were explored. The tube is heated for 40 minutes before water is pumped in. The temperature inside the tube was measured by placing thermocouples in the middle hole of each of the four sections without touching the surface of the tube. The elevated temperature inside the oven generates superheated steam. The superheated steam was applied over the first stage of baking at an average temperature of 245°C, then the steam flow stopped, and only the hot air was left in the rest of the cooking stages. A commercial aluminum tray with holes was used.

Magnetic induction

In magnetic induction heating occurs by passing an alternating current through a coil which produces the formation of a magnetic field that, when in contact with a ferromagnetic material, generates heat due to the creation of small microcurrents within the material. Heat is transferred from the ferromagnetic material to the food being cooked by heat conduction. The use of magnetic induction technologies is limited for applications in the chemical area such as evaporation of active, radioactive chemicals, and toxic substances in vacuum to produce coatings and nanoparticles. In food, it has been used, in addition to its domestic application, for drying or cooking thin products on conveyor belt ovens (El-Mashad & Pan, 2017). One of its main characteristics is that it has high heating rates.

Despite this, it has been little used for bakery products such as pizza. We propose the hypothesis that using magnetic induction at the beginning of pizza cooking will significantly increase its internal temperature allowing the baking time to be decreased in the first stage and that the use of IR radiation

will reduce the time in the last heating stage. In regular operation, the early stage of a conveyor oven is unused, using induction or any other technology to heat up the pizza previously could help us to take advantage of this space.

In this combination, a domestic induction stove (TU18 1000W, Turmix, Mexico City, Mexico) was used before introducing the pizza in the hot air oven with IR at the end (see Figure 12c). From the induction stove, the residence time was varied from 15 s to 1min 30s and the power in the range from 300W to 1000W, and the same levels were used as in the combination for hot air and IR resistance described in the next section. For this application, a 10in stainless steel 400 series 31 gauge circular tray made by us was used throughout the cooking process. In later sections, the parameters that produced pizzas with acceptable quality will be described.

Infrared

The use of infrared radiation in bakery products in combination with other technologies has been studied. It has been found that it generates a more uniform surface heating and produces bread with properties similar to those produced by conventional baking ovens and at a lower time (Ploteau et al., 2015). The high rate of heating in the form of radiation on the bread surface favors the rapid formation and browning of the crust. We propose the hypothesis that using IR radiation in the last cooking stage in combination with hot air in the first and second stages will decrease the cooking time because the reactions that produce the formation of the crust will be accelerated.

Figure 12d indicates how infrared technology was applied inside the hot air oven. A tube-shaped Incoloy coil resistor with a diameter of 6.7 mm, four sections, and a total width of 25.4 cm was used, radiating in the infrared spectrum (2000W, Zoppas Industries, Mexico City, Mexico). The power control of the resistance was done by means of a phase controller (Infitec PCSL6h 1834, NY, USA).

The resistance supply voltage was 220VAC. For the determination of the power consumed by the resistance, the current was measured with an oscilloscope (Fluke 128B, Fluke, Washington, USA) and a current clamp (Fluke i400, Fluke, Washington, USA), the voltage was measured with a multimeter ( Fluke 116 true RMS, Fluke, Washington, USA). The IR power was varied in the range of 500W to 2000W, the air temperature was fixed at 315 °C, and the band time moved in the range of 2min10s to 6min. In this combination, hot air was applied in the first and second cooking stage, and IR in the last cooking stage to cause the rapid formation of the pizza crust. The IR element was always on throughout the cooking process. The same tray was used as in the previous combinations.

In later sections, the parameters that produced pizzas with acceptable quality will be described.

4.2 Calculation of consumed energy and CO2 emissions

Carbon dioxide (CO2) is one of the most critical greenhouse gases cause of global warming. Therefore the decrease in emissions of this gas is of great interest to the authorities, researchers, and industry.

Savings made in terms of electrical energy have been shown to produce a reduction in CO2 emissions (Zampori & Dotelli, 2014). In this work, three parameters were established to evaluate the reduction in energy consumption and CO2 emissions: Energy Consumption (EC, kWh), CO2 Emission (EM, kg CO2 eq) and Emission Reduction percentage (ER). These parameters were obtained per pizza produced and evaluated for each combination of technolgies.

The EC was obtained with Equation 7.