Sensory and Quality Evaluation of Traditional Compared with Power Ultrasound Processed Corn (Zea Mays) Tortilla Chips

Power ultrasound reduces the traditional corn steeping time from 18 to 1.5 h during tortilla chips dough (masa) processing. This study sought to examine consumer (n = 99) acceptability and quality of tortilla chips made from the masa by traditional compared with ultrasonic methods. Overall appearance, flavor, and texture acceptability scores were evaluated using a 9‐point hedonic scale. The baked chips (process intermediate) before and after frying (finished product) were analyzed using a texture analyzer and machine vision. The texture values were determined using the 3‐point bend test using breaking force gradient (BFG), peak breaking force (PBF), and breaking distance (BD). The fracturing properties determined by the crisp fracture support rig using fracture force gradient (FFG), peak fracture force (PFF), and fracture distance (FD). The machine vision evaluated the total surface area, lightness (L), color difference (ΔE), Hue (°h), and Chroma (C*). The results were evaluated by analysis of variance and means were separated using Tukey's test. Machine vision values of L, °h, were higher (P < 0.05) and ΔE was lower (P < 0.05) for fried and L, °h were significantly (P < 0.05) higher for baked chips produced from ultra‐sonication as compare to traditional. Baked chips texture for ultra‐sonication was significantly higher (P < 0.05) on BFG, BPD, PFF, and FD. Fried tortilla chips texture were higher significantly (P < 0.05) in BFG and PFF for ultra‐sonication than traditional processing. However, the instrumental differences were not detected in sensory analysis, concluding possibility of power ultrasound as potential tortilla chips processing aid.     

Effect of added calcium hydroxide during corn nixtamalization on acrylamide content in tortilla chips

This study has analyzed the influence of lime (Ca(OH)₂) concentration used during nixtamalization process on the physicochemical properties of nixtamalized corn flours and its effect on the acrylamide content in tortilla chips prepared from them. Flours prepared from nixtamalized corn with 0.5 and 1.0 g/100 g of Ca(OH)₂ showed higher levels of insoluble fiber, lower concentrations of calcium and ash, and a pH close to neutral. The acrylamide content of tortilla chips was correlated with the physicochemical properties of the flours. The results showed that acrylamide content was mainly affected by the ash, calcium, soluble fiber concentration in flour, as well pH value (p < 0.05). In comparison with tortilla chips made of flours prepared from nixtamalized corn with lime concentration of 1.0 g/100 g, a reduction of 52 and 36% in acrylamide content in tortilla chips made with flours prepared from nixtamalized corn with lime concentrations of 1.5 and 2.0 g/100 g, was obtained. The results suggest that controlling the concentration of Ca(OH)₂ during nixtamalization process can be used as an effective strategy for reducing acrylamide formation in fried products produced from nixtamalized corn flour.     

Effect of processing of corn for production of masa,tortillas and tortilla chips on the scavenging capacity of reactive nitrogen species

The effect of processing conventional and pigmented corn into masa, tortilla and tortilla chips on the ability to scavenge nitric oxide (NO) and peroxynitrite (ONOO^?) was investigated. The level of retention of nitric oxide and peroxynitrite (mediated for inhibition of nitrite formation scavenging capacity for masa, tortilla and tortilla chips) ranged 56.2–78.2%, 67.4–45%, 40.3–62.0% (for nitric oxide) and 38.6–81.7%, 23.3–47.7%, 19.3–67.2% (for inhibition of nitrite formation), respectively. The antinitrosative activities were affected significantly (P > 0.05) by nixtamalisation, but not by processing masa into tortilla and tortilla chips (P > 0.05). The yellow variety and its corresponding products showed the greatest capacity to scavenge nitric oxide and inhibition of nitrite formation among conventional varieties and the purple variety ranked highest among the pigmented varieties.     

Mitigating effect of amaranth (Amarantus hypochondriacus) protein on acrylamide formation in foods

The effect of addition of amaranth flour and amaranth protein isolate to both a glucose/asparagine model system and real foods (cookies, fried tortilla chips, and baked tortilla chips) was studied to analyse the acrylamide mitigating potential of this underexploited plant with attractive nutraceutical properties. Addition of amaranth flour, with a relatively low protein content (16.45%), did not mitigate acrylamide in either the model system or the studied foods. On the contrary, addition of amaranth protein isolate decreased acrylamide content by 35–40% in the model system, 89% in cookies, 51% in fried tortilla chips, and 62% in baked tortilla chips. This acrylamide reduction was obtained without change in the colour or the texture of the cookies. On the contrary, colour remained unchanged in tortilla chips, but the addition of amaranth protein isolate increased the hardness (16–36%) of the produced tortillas. Although a much more detailed sensory evaluation of cookies and tortilla chips prepared using amaranth protein isolate is needed, the above results suggest that the use of amaranth protein may be an interesting way to both mitigate acrylamide formation and improve nutritional properties of foods.     

Computer vision‐based analysis of foods: A non‐destructive colour measurement tool to monitor quality and safety

Computer vision‐based image analysis has been widely used in food industry to monitor food quality. It allows low‐cost and non‐contact measurements of colour to be performed. In this paper, two computer vision‐based image analysis approaches are discussed to extract mean colour or featured colour information from the digital images of foods. These types of information may be of particular importance as colour indicates certain chemical changes or physical properties in foods. As exemplified here, the mean CIE a^* value or browning ratio determined by means of computer vision‐based image analysis algorithms can be correlated with acrylamide content of potato chips or cookies. Or, porosity index as an important physical property of breadcrumb can be calculated easily. In this respect, computer vision‐based image analysis provides a useful tool for automatic inspection of food products in a manufacturing line, and it can be actively involved in the decision‐making process where rapid quality/safety evaluation is needed. © 2013 Society of Chemical Industry     

Functional properties of tortillas with triticale flour as a partial substitute of nixtamalized corn flour

The effect of partial substitution of nixtamalized corn flour for triticale flour in functional properties of dough and tortillas was evaluated. Seven blends of refined triticale and nixtamalized corn flour (NCF) were tested in a traditional process of tortilla production. Triticale flour significantly reduced water absorption of dough, tortillas cooking time and yield. NCF-Triticale flour blends for making tortillas using water to 80 °C improves the consistency and cohesiveness of dough. A mix containing more than 25 g of triticale flour per 100 g of flour develop inadequate adhesiveness of the dough, which is inconvenient when using the manual press. The extensibility of tortilla blends was lower than pure NCF tortillas. The advisable proportion for triticale flour used as a partial substitute of nixtamalized corn flour was 100 g kg⁻¹ (flavor, color, texture and an acceptable adhesiveness).     

Image analysis of changes in surface color of chocolate

Blooming or the migration of fat to the surface of chocolate results in color changes and development of non-uniform color patterns. These phenomena were assessed during storage of milk chocolate tablets (cycling temp. between 16 and 28 °C for 52 days) by a computer vision system and image analysis. Eight features were extracted from images (L*, a* and b* values, whiteness index, chroma, hue, % bloom and energy of Fourier). Major changes occurred after day 36 of storage, coincidental with visual perception. Initially, white specks emerged on the brown background but were superseded by the development of a whitish color extending over most of the surface. L*, whiteness index, a* and chroma correlated well with values taken with a commercial colorimeter (R² > 0.70). Changes in image texture (energy of Fourier) followed a similar trend as color changes. The sequential forward selection strategy allowed correct classification of 97.8% of samples into four classes with only five features. The computer vision system has the capability to quantify overall changes as well as particular features over the whole chocolate surface thus enabling customization and standardization for quality assessment.     

Modeling preference of commercial toasted white corn tortilla chips using proportional odds models

Eleven commercial toasted white corn tortilla chip products from the United States were evaluated by a group of 80 consumers of age 18–35 and by a trained sensory panel. Proportional odds models in conjunction with principal components were used for internal and external preference modeling of tortilla chip consumer overall acceptance. The internal preference modeling showed that flavor was the most important attribute to consumer overall acceptance followed by the interaction of appearance by flavor and texture. The external preference modeling showed that one flavor attribute (salt aftertaste) and one texture attribute (crispness) contributed significantly to increase consumer overall acceptance, whereas one appearance attribute (instrumental color a¹) significantly lowered consumer overall acceptance. The information reported in this study is important to the tortilla chip industry to produce tortilla chips with greater consumer acceptability. This study implies that proportional odds model using principal components is an alternative tool for consumer preference modeling.     

Development of a computer vision system to measure the color of potato chips

The objective of this research was to design and implement an inexpensive computer vision system for measuring the color of a highly heterogeneous food material not only in shape as well in color such as potato chips in L^*a^*b^* units from RGB images. The system was composed of (i) a digital color camera for acquiring the images in a digital format, (ii) a computer for storage the images, (c) image analysis routines integrated into a software programmed in Matlab that converts the color RGB of the food image into L^*a^*b^* units. In this way the color of potato chips can be calculated in L^*a^*b^* units over representative areas and in a reproducible way. The kinetics of color changes in potato slices during frying at four temperatures was followed using the implemented computer vision system (CVS). Color values in L^*a^*b^* units were recorded at different sampling times during frying at the four oil temperatures using the total color change parameter (ΔE). Chips fried at higher temperatures get darker as expected and showed by the CVS. The implemented computer vision system can be used to study as well foods different from potato chips by selecting their proper settings for image acquisition and digital image processing.     

Quality classification of corn tortillas using computer vision

Computer vision is playing an increasingly important role in automated visual food inspection. However, quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. Cross-validation yielded a performance of 95% in the classification of the five hedonic sub-classes. Additionally, using only 10 of the selected features and a simple statistical classifier, it was possible to determine the origin of the tortillas with a performance of 96%. We believe that the proposed framework opens up new possibilities in the field of automated visual inspection of tortillas.     

Colour and image texture analysis in classification of commercial potato chips

The images of commercial potato chips were evaluated for various colour and textural features to characterize and classify the appearance and to model the quality preferences of a group of consumers. Features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers’ preferences. Entropy of a¹ and V and energy of b¹ from images of the total chip surface, average and variance of H and correlation of V from the images of spots and/or defects (if they are present), and average of L¹ from clean images (chips free of spots and/or defects) showed the best correspondence with the four proposed appearance quality groups (A: ‘pale chips’, B: ‘slightly dark chips’, C: ‘chips with brown spots’, and D: ‘chips with natural defects’), giving classification rates of 95.8% for training data and 90% for validation data when linear discriminant analysis (LDA) was used as a selection criterion. The inclusion of independent colour and textural features from images of brown spots and/or defects and their clean regions of chips improved the resolution of the classification model and in particular to predict ‘chips with natural defects’. Consumers’ preferences showed that in spite of the ‘moderate’ agreement among raters (Kappa-value = 0.51), textural features have potential to model consumer behaviour in the respect of visual preferences of potato chips. A stepwise logistic regression model was able to explain 86.2% of the preferences variability when classified into acceptable and non-acceptable chips.     

THERMAL and PHYSICAL PROPERTIES of TORTILLA CHIPS AS A FUNCTION of FRYING TIME

The objective of this study was to determine the thermal conductivity, specific heat, density (true and bulk), and the texture characteristics of tortilla chips and to study the effects of moisture content, oil content, and frying time on these properties. Tortilla chips were fried in fresh soybean oil for different times at 190C. Results showed that the values of thermal conductivity of tortilla chips decreased from 0.23 to 0.09 W/mC, specific heat from 3.36 to 2.31 kJ/kgC, and bulk density from 880 to 579 kg/m³ as frying time increased. In general, the crispness of tortilla chips increased as frying time increased.     

Prediction of beef eating quality from colour, marbling and wavelet texture features

Beef longissimus dorsi colour, marbling fat and surface texture are long established properties that are used in some countries by expert graders to classify beef carcasses, with subjective and inconsistent decision. As a computer vision system can deliver objective and consistent decisions rapidly and is capable of handling a greater variety of image features, attempts have been made to develop computerised predictions of eating quality based on these and other properties but have failed to adequately model the variation in eating quality. Therefore, in this study, examination of the ribeye at high magnification and consideration of a broad range of colour and marbling fat features was used to attempt to provide better information on beef eating quality. Wavelets were used to describe the image texture of the beef surface at high magnification rather than classical methods such as run lengths, difference histograms and co-occurrence matrices. Sensory panel and Instron analyses were performed on duplicate steaks to measure the quality of the beef. Using the classical statistical method of partial least squares regression (PLSR) it was possible to model a very high proportion of the variation in eating quality (r² = 0.88 for sensory overall acceptability and r² = 0.85 for 7-day WBS). Addition of non-linear texture terms to the models gave some improvements.     

Estimation of crispness and consumer acceptance of fried potato chips by mechanical and acoustic measurements

Fried potato chips retaining various moisture contents (MCs) (2.21–9.20%) were analysed to estimate the intensity of crispness and consumer acceptance by texture and acoustic measurements. The MC of the chips was highly correlated with the mechanical maximum force (MMF) in the texture measurement, total area (MTA) and number of sound peaks (NSP) in the acoustic measurement. The intensities of crispness and consumer acceptance decreased as the MC of potato chips increased. For the predictive models established, the combined use of mechanical and acoustic parameters was shown to better predict sensory crispness intensity [R² = 0.975, root mean square error of prediction (RMSEP) = 0.138] and consumer overall liking (R² = 0.966, RMSEP = 0.111) than either parameter alone. Based on the instrumental‐sensory crispness equivalent table established, the estimated values of the MTA were below 71.24, while the NSP should be above 22.81 to meet ‘slightly like’ category of consumer acceptance.     

Effect of Degree of Starch Gelatinization on Quality Attributes of Fried Tortilla Chips

ABSTRACT: The effect of degree of starch gelatinization on the quality attributes of tortilla chips was studied. Three different samples were prepared: control (45% of starch gelatinized before frying), freeze-dried (5% of starch gelatinized before frying), and steamed-baked tortilla chips (87% starch gelatinized before frying). Compared to the control chips, the steamed-baked tortillas produced chips with lower oil content. They shrunk the most and showed a high degree of puffiness and lower crunchiness. Their texture was harder and not very crunchy. The majority of the oil in these chips was located at the surface. The freeze-dried tortilla yielded chips with high oil content. The texture was soft, and the porosity was very low, providing unacceptable characteristics in tortilla chips.     

Prediction of wheat tortilla quality using multivariate modeling of kernel,flour, and dough properties

Traditional wheat quality methods for bread have poor predictive power for flatbread quality, which impedes genetic improvement of wheat for the growing market. We used a multivariate discriminant analysis to predict tortilla quality using a set of 16 variables derived from kernel properties, flour composition, and dough rheological properties of 187 experimental hard wheat samples grown across Texas. A discriminant rule (suitability for tortillas = diameter > 165 mm + day 16 flexibility score > 3.0) was used to classify samples. Multivariate normal distribution of the data was established (Shapiro–Wilk p > 0.05). Logistic regression and stepwise variable selection identified an optimum model comprising kernel weight, glutenin–gliadin ratio, insoluble polymeric proteins, and dough extensibility and stress relaxation parameters, as the most important variables. Cross-validation indicated 83% model prediction efficiency. This work provides important insight on potential targets for wheat quality genetic improvement for tortillas and specialty product market.Industrial relevanceTortillas and other flatbread manufacturers currently use wheat developed for other commodities and rely on trial and error, and use of various additives to optimize product quality. Genetic development of wheat for these markets is impeded by lack of knowledge of specific grain quality parameters to target. With the growing demand for clean label and healthy offerings by consumers, the industry is looking for natural ingredients with improved functionality. This work provides the first insight into the specific wheat composition and functional parameters that can predict tortilla quality.     

Deep Frying Performance of Enzymatically Synthesized Palm-Based Medium- and Long-Chain Triacylglycerols (MLCT) Oil Blends

The main aim of this work was to assess the frying strength of the enzymatically synthesized palm-based medium- and long-chain triacylglycerols (MLCT) oil with the aid of different antioxidants under deep-frying conditions. Palm-based MLCT oil in the presence of synthetic or natural antioxidants showed significantly better (P < 0.05) thermal resistance and oxidative strength than refined, bleached, and deodorized (RBD) palm olein throughout the five consecutive days of frying. Rancimat induction period, free fatty acid content, anisidine value, E^\text1% _1\textcm E^{{\text{1\% }}} _{1{\text{cm}}} at 232 and 268 nm, color, percentage of oil uptake, and viscosity measurement can be used as oil quality parameters to indicate the degree of oil deterioration under continuous stressed frying conditions. No significant changes (P > 0.05) in the saturated/unsaturated fatty acids ratio across frying periods indicated good oxidative stability of the palm-based MLCT oil. Due to the polarity of medium- and long-chain triacylglycerols in palm-based MLCT oil, total polar compounds determination may not be a suitable oil quality measures. Sensory evaluation of fried chips showed no significant differences (P > 0.05) between chips fried in RBD palm olein and palm-based MLCT oil over the 3-month storage period.     

Effects of Extruded Sugar Bagasse Blend on Yogurt Quality

Extruded blends (EB) of whey protein concentrate 35 (WPC 35)–corn starch–sugarcane bagasse with 0%, 25%, and 50% substituted yogurt formulations were evaluated for some functional characteristics. Yogurts substituted with EB showed higher viscosity and lower syneresis index than that without EB. During storage for 48 h, the syneresis values of yogurt with and without EB decreased. The pH values and color differences (ΔE) decreased slightly in the yogurts formulated with EB. However, the color differences were not statistically significant (p > 0.05) and were also acceptable to the judges. In general, yogurts substituted with EB at different levels had sensory attributes of taste, acidity, texture, and viscosity scores similar to those observed for yogurt with no EB. Scores of yogurts were not affected significantly with substitution of EB at all the levels.     

Effects of Characteristics Changes of Collagen on Meat Physicochemical Properties of Beef Semitendinosus Muscle during Ultrasonic Processing

Low-frequency and high-power ultrasound (40 kHz, 1,500 W) was tested for its effects on the characteristics of intramuscular heat-insoluble collagen and meat quality and textural properties of beef semitendinosus muscle. Meat steaks (2.5 × 5.0 × 5.0 cm, 100 ± 5 g) were sonicated for 10, 20, 30, 40, 50, and 60 min, respectively. Characteristics changes of collagen and meat quality and texture were estimated. The results indicated that ultrasound treatment had no significant (P > 0.05) effects on L* (lightness) and a* (redness) values but decreased b* (yellowness) value significantly when sonicated for 30 min (minimum 6.98). Ultrasound treatment significantly (P < 0.01) reduced the muscle fiber diameter and filtering residues but had no significant effects on the content of heat-insoluble collagen. Significant differences (P < 0.05) in β-galactosidase and β-glucuronidase activity were found between ultrasound treated for 10 min (reached the minimum were 5.2 × 10⁻³ and 1.6 × 10⁻³ umol∙ml⁻¹∙min⁻¹, respectively) and control samples. Thermal characteristics analysis of collagen suggested that ultrasound treatment weaken the average stability of collagen. After ultrasound treatment, collagenous fibers were disordered and staggered significantly; fiber arrangement became loose; and the denaturing, granulation, and aggregation of collagen fiber appeared in the extracellular space. Those changes on collagen characteristics had significant effects on meat textural properties. The results suggested that low-frequency and high-power sonication had a significant effect on collagen characters and meat textural properties.