Use of hyperspectral imaging for cake moisture and hardness prediction

Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on-line, real-time, stand-off cake quality monitoring

Spectral study of heat treatment process of wheat flour by VIS/SW-NIR image system

The capability of the VIS/SW-NIR (visible/short wave near infrared) hyperspectral imaging system to characterize the heat treatment process of cake wheat flour was studied. Combinations of heat treatments of flour were run at different temperatures (80, 100 and 130 °C) and for various times (10, 20 and 30 min). The resulting treated flours were analyzed by the imaging technique. The hyperspectral results, studied by multivariate statistical methods, showed a pattern evolution of the flours treated by different heat treatments. The wavelengths that contributed the most, and implied in the differentiations, were detected. The selection of wavelengths allowed us to optimize the analysis, which reduced from 54 to 6 wavelengths. To ensure that the VIS/SW-NIR information depended on the heat treatment influence on flours, cakes were produced and characterized according to height, mass loss during the baking process, crumb structure and textural properties. The VIS/SW-NIR imaging analysis was capable of following the changes that occurred during the different heat treatments of flours. VIS/SW-NIR was applied to determine and adjust the heat treatment process variables to improve the features of flours during the cake production process.Verdú Amat, S.; Ivorra Martínez, E.; Sánchez Salmerón, AJ.; Barat Baviera, JM.; Grau Meló, R. (2016). Spectral study of heat treatment process of wheat flour by VIS/SW-NIR image system. Journal of Cereal Science. 71:99-107. doi:10.1016/j.jcs.2016.08.008S991077

Aplicación del análisis de imagen hiperespectral y tridimensional al control de procesos y productos en la industria harinera y sus derivados

Tesis por compendio[EN] This work is focused on studying of hyperspectral and structured light based tridimensional image analysis about their application on quality and process control of cereal flour industry and derived products. The structured light based tridimensional image analysis has been used to develop a bread dough dynamic fermentation control system. Descriptors obtained from dough shape evolution were used to describe differences between wheat flour batches during fermentation process. In the same way, that system was used to characterize the effect of new ingredients on fermentation process. Those behaviors were analyzed joint to the intern structure of dough during the process, establishing relationships between it and the tridimensional information. Differences in fermentation process were also studied using hyperspectral image analysis. Flours were analyzed using the obtained diffuse reflectance spectra, which contained information within 400-1000 nm of wavelength range. Differences in several spectral bands were correlated with fundamental components of flours such as gluten. That spectral characterization of flours was used to detect adulterations with different grains. Adulterations until 2, 5% of oat, sorghum and corn were detected both flour and bread crumb. The hyperspectral image analysis was also used to control the heat treatment of wheat and oat flours, where spectral information was related to properties of end products.[ES] El presente trabajo está centrado en el estudio de los sistemas de análisis de imagen hiperespectral y tridimensional basado en luz estructurada para su aplicación en el control de procesos y calidad de la industria harinera y de sus derivados. El sistema de imagen tridimensional basado en luz estructurada fue la base para el desarrollo de un sistema de monitorización en continuo de la fase de fermentación de masas panarias. A partir de descriptores desarrollados relacionados con la variación de la forma del producto durante la operación, se establecieron diferencias entre lotes de harinas de trigo y describió el comportamiento de masas reformuladas con nuevos ingredientes. Dicho comportamiento fue analizado en conjunto con la estructura interna de la masa durante la operación, estableciendo relaciones concretas entre esta y la información obtenida a partir de las imágenes. Las diferencias de comportamiento durante la operación de fermentación también fueron estudiadas mediante el sistema de imagen hiperespectral. En este caso, las harinas fueron analizadas directamente mediante imágenes espectrales, obteniendo espectros de reflectancia difusa en el rango de longitudes de onda 400-1000, donde se observaron diferencias en determinadas bandas del espectro. Dichas diferencias fueron correlacionadas con determinados componentes fundamentales como el gluten. La caracterización espectral de la harina de trigo se utilizó posteriormente para la detección de cereales diferentes mezclados con esta. Adulteraciones de hasta un 2,5% de avena, sorgo y maíz fueron detectadas tanto en harina como en panes de trigo. El análisis de imagen hiperespectral también ha sido aplicado al control del tratamiento térmico de harinas de trigo y avena, donde se ha conseguido relacionar la información espectral con las características del producto final.[CA] El present treball està centrat en l'estudi dels sistemes d'anàlisis d'imatge hiperespectral i tridimensional basat en llum estructurada per a la seua aplicació en el control de processos i qualitat de la indústria farinera i dels seus derivats. El sistema d'imatge tridimensional basat en llum estructurada va ser la base per al desenvolupament d'un sistema de monitoratge en continu de la fase de fermentació de masses panaries. A partir dels descriptors desenvolupats relacionats amb la variació de la forma del producte durant l'operació, es van establir diferències entre lots de farines de blat i es va descriure el comportament de masses reformulades amb nous ingredients. Aquest comportament va ser analitzat en conjunt amb l'estructura interna de la massa durant l'operació, establint relacions concretes entre aquesta i la informació obtinguda a partir de les imatges. Les diferències de comportament durant l'operació de fermentació també van ser estudiades mitjançant el sistema d'imatge hiperespectral. En aquest cas, les farines van ser analitzades directament mitjançant imatges espectrals, obtenint espectres de reflectància difusa en el rang de longituds d'ona 400-1000, on es van observar diferències en determinades bandes de l'espectre. Aquestes diferències van ser correlacionades amb determinats components fonamentals com el gluten. La caracterització espectral de la farina de blat es va utilitzar posteriorment per a la detecció de cereals diferents barrejats amb aquesta. Adulteracions de fins a un 2,5% de civada, sorgo i dacsa van ser detectades tant en farina com en pans de blat. L'anàlisi d'imatge hiperespectral també ha sigut aplicat al control del tractament tèrmic de farines de blat i civada, on s'ha aconseguit relacionar la informació espectral amb les característiques del producte final.Verdú Amat, S. (2016). Aplicación del análisis de imagen hiperespectral y tridimensional al control de procesos y productos en la industria harinera y sus derivados [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65354TESISPremios Extraordinarios de tesis doctoralesCompendi

Nir Spectral Techniques and Chemometrics Applied to Food Processing

Tesis por compendio[ES] Las técnicas rápidas, no destructivas y libres de químicos tienen una demanda creciente en muchos campos de la industria. Las técnicas de espectroscopia de infrarrojo cercano (NIRS) y imágenes hiperespectrales NIR (NIR-HSI) han mostrado un gran potencial para determinar los parámetros de calidad de los alimentos, autenticar productos alimenticios, detectar el fraude, entre otras. En la NIRS, las medidas se toman en puntos específicos, detectando solo una pequeña porción; en la NIR-HSI, la información espectral y espacial se combinan, lo que la convierte en una opción adecuada para muchos productos alimenticios, ya que son matrices muy heterogéneas. Por lo tanto, este estudio tuvo como objetivo revisar la aplicación de NIRS (dispersivos), NIR de Transformada de Fourier (FT) y HSI en la evaluación de los parámetros de calidad de harina de trigo y productos a base de trigo, así como para la autenticación y determinación de la composición de estos productos. Además, este trabajo tuvo como objetivo identificar y clasificar diferentes tipos de muestras de fibra agregadas a la semolina y pasta producidas por estas formulaciones, y monitorear el proceso de cocción de esta pasta enriquecida en fibra mediante técnicas espectrales. Además, se objetivó aplicar HSI a otro producto en polvo, por lo que se cuantificó el contenido de pectina en las cáscaras de naranja. Primero, se adquirieron espectros NIR para comparar la precisión en la clasificación de muestras enriquecidas con fibra, para cuantificar la cantidad de estas fibras y verificar su distribución en muestras de semolina. Para la clasificación se utilizaron el Análisis de Componentes Principales (PCA) y el Soft Independent Modelling of Class Analogy (SIMCA). Los modelos de regresión de mínimos cuadrados parciales (PLSR) aplicados a espectros NIR-HSI mostraron R²P entre 0,85 y 0,98 y RMSEP entre 0,5 y 1, y los modelos se utilizaron para construir los mapas químicos para verificar la distribución de fibra en las superficies de las muestras. Además, se probó el NIR-HSI junto con Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) para investigar la capacidad de evaluación, resolución y cuantificación de la distribución de fibra en la pasta. Los resultados mostraron R²P entre 0.28 y 0.89,% de falta de ajuste (LOF) <6%, varianza explicada sobre 99% y similitud entre espectros puros y recuperados sobre 96% y 98%. Además, se probó VIS/NIR-HSI en el modo de transmisión como una alternativa objetiva para la clasificación de muestras de pasta según el tiempo de cocción. El análisis discriminante lineal (LDA) mostró valores de sensibilidad y especificidad entre 0,14-1,00 y 0,51-1,00, respectivamente, y una tasa de ausencia de error (NER) superior a 0,62. El análisis discriminante de mínimos cuadrados parciales (PLSDA) mostró valores de sensibilidad y especificidad entre 0,67-1,00 y 0,10-1,00, respectivamente, y NER superiores a 0,80. Los resultados de este trabajo mostraron que la técnica NIR-HSI se puede utilizar para la identificación y cuantificación de la fibra agregada a la semolina. Además, NIR-HSI y MCR-ALS pueden identificar la fibra en la pasta. La HSI en el modo de transmisión demostró ser una técnica adecuada como alternativa objetiva para la clasificación de muestras de pasta según el tiempo de cocción como una forma de automatizar la determinación de los atributos de la pasta. La determinación del contenido de pectina en cáscaras de naranja se investigó usando NIR-HSI. LDA mostró mejores resultados de discriminación considerando tres grupos: bajo (0-5%), intermedio (10-40%) y alto (50-100%) contenido. Los modelos PLSR basados en espectros completos mostraron mayor precisión (R2> 0,93, RMSEP entre 6,50 y 9,16% de pectina) que los basados en pocas longitudes de onda seleccionadas (R2 entre 0,92 y 0,94, RMSEP entre 8,03 y 9,73% de pectina). Los resultados demuestran el potencial de NIR-HSI para cuantificar el contenido de pectina en las cáscaras de naranja, proporcionando una técnica valiosa para los productores de naranja y las industrias de procesamiento.[CA] Les tècniques ràpides, no destructives i lliures de químics tenen una demanda creixent en molts camps de la indústria. Les tècniques d'espectroscopia d'infraroig proper (NIRS) i d'imatges hiperespectrals NIR (NIR-HSI) han demostrat tindre un gran potencial per a determinar paràmetres de qualitat d'aliments, autenticar productes alimentaris, detectar frau entre altres aplicacions. Mentre que en la NIRS proper les mesures es prenen en punts específics de la mostra i es detecta una porció menuda, en la HSI es combina informació espectral i espacial de tal manera que és una opció adient per a molts tipus de productes alimentaris, ja que són matrius molt heterogènies. Per tant, este estudi va tindre com objectiu revisar tota l'aplicació de NIRS (dispersius), NIR de Transformada de Fourier (FT) i HSI en l'avaluació dels paràmetres de qualitat de la farina de blat i els productes a base de blat, així com per a l'autenticació i determinació de la composició d'estos productes. A més a més, este estudi va tindre com objectiu identificar i classificar diferents tipus de mostres de fibra afegides a la semolina i pasta produïdes per formulació de fibra i semolina, i monitorar mitjançant tècniques espectrals el procés de cocció d'aquesta pasta enriquida amb fibra. A més, este treball va tindre com objectiu aplicar HSI a un altre producte en pols, de tal manera que es va quantificar el contingut de pectina en les corfes de taronja. Primer, es van adquirir espectres NIR per comparar la precisió en la classificació de mostres enriquides amb fibra, per quantificar estes fibres i verificar la seua distribució en mostres de sèmola. Per a la classificació es van emprar l'Anàlisi de Components Principals (PCA) i el SIMCA (Soft Independent Modelling of Class Analogy). Els models de regressió de mínims quadrats parcials (PLSR) aplicats a espectres NIR-HSI mostraren R²P entre 0,85 i 0,98 i RMSEP entre 0,5 i 1% de contingut de fibra, i els models s'utilitzaren per construir els mapes químics per verificar la distribució de fibra en les superficies de les mostres. Així mateix, es va provar NIR-HSI amb Multivariate Curve Resolution-Alternating Least Square (MCR-ALS) per a investigar la capacitat d'avaluació, resolució i quantificació de la distribució de fibra en la pasta enriquida. Els resultats mostraren un R²P entre 0,28 i 0,89%, lack of fit (LOF) 0,93, RMSEP entre 6,50 i 9,16% de pectina) que els basats en longituds d’ona seleccionades (R2 entre 0,92 i 0,94, RMSEP entre 8,03 i 9,73% de pectina). Els resultats demostren el potencial de NIR-HSI per a quantificar el contingut de pectina en corfa de taronja i proporcionen una tècnica valuosa per als productors de taronja i les indústries de processament.[EN] Fast, non-destructive and chemical-free techniques are in increasing demand in many fields of the industry. Near-infrared spectroscopy (NIRS) and NIR hyperspectral imaging (NIR-HSI) techniques have shown great potential in determining food quality parameters, authenticating food products, detecting food fraud, among many other applications. While in near infrared spectroscopy, the measurements are taken at specific points on the sample, detecting only a small portion; in hyperspectral imaging, spectral and spatial information are combined, making it a suitable choice for many food products, since they are very heterogeneous matrices. Therefore, this study aimed to review all the application of (dispersive) NIRS, Fourier Transform (FT) NIR, and HSI in assessing wheat flour and wheat-based products quality parameters, as well for the authentication and determination of composition of these products. Moreover, this work aimed to identify and classify different types of fibre samples added to the semolina and pasta produced by semolina-fibre formulations, and to monitor the cooking process of this fibre-enriched pasta by spectral techniques. In addition, this work had the aim of applying HSI to other powdered product, so the pectin content in orange peels was quantified. First, NIR spectra were acquired to compare the accuracy in the classification of fibre-enriched samples, to quantify the amount of these fibres and verify their distribution on semolina samples. Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were used for classification. Partial Least Squares Regression (PLSR) models applied to NIR-HSI spectra showed R2P between 0.85 and 0.98, and RMSEP between 0.5 and 1% of fibre content, and the models were used to construct the chemical maps to check the fibre distribution on the samples surface. Moreover, NIR-HSI together with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), was tested to investigate the ability for the evaluation, resolution and quantification of fibre distribution in enriched pasta. Results showed coefficient of determination of validation (R²V) between 0.28 and 0.89, % of lack of fit (LOF) 0.93, RMSEP between 6.50 and 9.16% of pectin) than those based on few selected wavelengths (R² between 0.92 and 0.94, RMSEP between 8.03 and 9.73%). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries.This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior- Brasil (CAPES) [Finance Code 001]; São Paulo Research Foundation (FAPESP) [grant numbers 2015/24351-2, 2017/17628-3, 2019/06842- 0]; and by projects AEI PID2019-107347RR-C31 and PID2019-107347RR-C32, and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014-2020. The authors would like to thank Nutrassim Food Ingredients company for the donation of the fibre samples, the support provided by Enrique Aguilar María, Carlos Alberto Velasquez Hernández, Diego Hernández Catalán, Carlos Ruiz Catalá and Andrés Estuardo Prieto López during system installation, experimental analysis and data acquisition.Teixeira Badaró, A. (2021). Nir Spectral Techniques and Chemometrics Applied to Food Processing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178758Compendi

Texture and color analysis of lentils and rice for instant meal using image processing techniques

Typical approaches for measuring color and texture properties are mostly timeconsuming. An image-based method was used to evaluate texture and color in lentils and rice subjected to freeze-drying for an instant meal. Cooked and cooked freeze-dried rehydrated lentils and rice were analyzed by scanning electron microscopy. Texture properties were analyzed by texture analyzer and image analysis. Color was performed with a digital camera. Significant differences for color and texture (P < 0.05) were observed for cooked and cooked freeze-dried rehydrated lentils and rice. A linear trend with a linear correlation was applied for mechanical and image features. Results showed that image features such as contrast, correlation, energy and homogeneity calculated from Gray-level co-occurrence matrix had high correlations with mechanical features of hardness, adhesiveness, chewiness and gumminess for lentils, and for rice, mechanical features adhesiveness, gumminess and image features homogeneity and contrast. With this approach to quality, image processing techniques can be a useful tool.Instituto de Tecnología de AlimentosFil: Pieniazek, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Sancho, Ana Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Tecnología de Alimentos, Argentina.Fil: Messina, Valeria. Instituto de Investigaciones Científicas y Técnicas para la Defensa. Departamento de Investigaciones en Sólidos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Advances in Postharvest Process Systems

This Special Issue presents a range of recent technologies and innovations to help the agricultural and food industry to manage and minimize postharvest losses, enhance reliability and sustainability, and generate high-quality products that are both healthy and appealing to consumers. It focuses on three main topics of food storage and preservation technologies, food processing technologies, and the applications of advanced mathematical modelling and computer simulations. This presentation of the latest research and information is particularly useful for people who are working in or associated with the fields of agriculture, the agri-food chain and technology development and promotion

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

In recent decades, the quality and safety of fruits, vegetables, cereals, meats, milk, and their derivatives from processed foods have become a serious issue for consumers in developed as well as developing countries. Undoubtedly, the traditional methods of inspecting and ensuring quality that depends on the human factor, some mechanical and chemical methods, have proven beyond any doubt their inability to achieve food quality and safety, and thus a failure to achieve food security. With growing attention on human health, the standards of food safety and quality are continuously being improved through advanced technology applications that depend on artificial intelligence tools to monitor the quality and safety of food. One of the most important of these applications is imaging technology. A brief discussion in this chapter on the utilize of multiple imaging systems based on all different bands of the electromagnetic spectrum as a principal source of various imaging systems. As well as methods of analyzing and reading images to build intelligence and non-destructive systems for monitoring and measuring the quality of foods

Roadmap of cocoa quality and authenticity control in the industry: a review of conventional and alternative methods

[EN] Cocoa (Theobroma cacao L.) and its derivatives are appreciated for their aroma, color, and healthy properties, and are commodities of high economic value worldwide. Wide ranges of conventional methods have been used for years to guarantee cocoa quality. Recently, however, demand for global cocoa and the requirements of sensory, functional, and safety cocoa attributes have changed. On the one hand, society and health authorities are increasingly demanding new more accurate quality control tests, including not only the analysis of physicochemical and sensory parameters, but also determinations of functional compounds and contaminants (some of which come in trace quantities). On the other hand, increased production forces industries to seek quality control techniques based on fast, nondestructive online methods. Finally, an increase in global cocoa demand and a consequent rise in prices can lead to future cases of fraud. For this reason, new analytes, technologies, and ways to analyze data are being researched, developed, and implemented into research or quality laboratories to control cocoa quality and authenticity. The main advances made in destructive techniques focus on developing new and more sensitive methods such as chromatographic analysis to detect metabolites and contaminants in trace quantities. These methods are used to assess cocoa quality; study new functional properties; control cocoa authenticity; or detect frequent emerging frauds. Regarding nondestructive methods, spectroscopy is the most explored technique, which is conducted within the near infrared range, and also within the medium infrared range to a lesser extent. It is applied mainly in the postharvest stage of cocoa beans to analyze different biochemical parameters or to assess the authenticity of cocoa and its derivatives.The authors wish to acknowledge the financial assistance provided by the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). Maribel Quelal Vásconez thanks the Ministry Higher Education, Science, Technology, and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant.Quelal-Vásconez, MA.; Lerma-García, MJ.; Pérez-Esteve, É.; Talens Oliag, P.; Barat Baviera, JM. (2020). Roadmap of cocoa quality and authenticity control in the industry: a review of conventional and alternative methods. Comprehensive Reviews in Food Science and Food Safety. 19(2):448-478. https://doi.org/10.1111/1541-4337.12522S448478192Abdullahi, G., Muhamad, R., Dzolkhifli, O., & Sinniah, U. R. (2018). Analysis of quality retentions in cocoa beans exposed to solar heat treatment in cardboard solar heater box. Cogent Food & Agriculture, 4(1), 1483061. doi:10.1080/23311932.2018.1483061Abt, E., Fong Sam, J., Gray, P., & Robin, L. P. (2018). Cadmium and lead in cocoa powder and chocolate products in the US Market. Food Additives & Contaminants: Part B, 11(2), 92-102. doi:10.1080/19393210.2017.1420700Acierno, V., Alewijn, M., Zomer, P., & van Ruth, S. M. (2018). Making cocoa origin traceable: Fingerprints of chocolates using Flow Infusion - Electro Spray Ionization - Mass Spectrometry. Food Control, 85, 245-252. doi:10.1016/j.foodcont.2017.10.002Aculey, P. C., Snitkjaer, P., Owusu, M., Bassompiere, M., Takrama, J., Nørgaard, L., … Nielsen, D. S. (2010). Ghanaian Cocoa Bean Fermentation Characterized by Spectroscopic and Chromatographic Methods and Chemometrics. Journal of Food Science, 75(6), S300-S307. doi:10.1111/j.1750-3841.2010.01710.xAfoakwa, E. O., Paterson, A., Fowler, M., & Ryan, A. (2009). Matrix effects on flavour volatiles release in dark chocolates varying in particle size distribution and fat content using GC–mass spectrometry and GC–olfactometry. Food Chemistry, 113(1), 208-215. doi:10.1016/j.foodchem.2008.07.088Afoakwa, E. O., Quao, J., Takrama, J., Budu, A. S., & Saalia, F. K. (2011). Chemical composition and physical quality characteristics of Ghanaian cocoa beans as affected by pulp pre-conditioning and fermentation. Journal of Food Science and Technology, 50(6), 1097-1105. doi:10.1007/s13197-011-0446-5Alander, J. T., Bochko, V., Martinkauppi, B., Saranwong, S., & Mantere, T. (2013). A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety. International Journal of Spectroscopy, 2013, 1-36. doi:10.1155/2013/341402Álvarez, C., Pérez, E., Cros, E., Lares, M., Assemat, S., Boulanger, R., & Davrieux, F. (2012). The Use of near Infrared Spectroscopy to Determine the Fat, Caffeine, Theobromine and (−)-Epicatechin Contents in Unfermented and Sun-Dried Beans of Criollo Cocoa. Journal of Near Infrared Spectroscopy, 20(2), 307-315. doi:10.1255/jnirs.990Agricultural and Processed Food Products Export Development Authority (APEDA). (2015).Export statement. Retrieved fromhttp://agriexchange.apeda.gov.in/indexp/exportstatement.aspxAprotosoaie, A. C., Luca, S. V., & Miron, A. (2015). Flavor Chemistry of Cocoa and Cocoa Products-An Overview. Comprehensive Reviews in Food Science and Food Safety, 15(1), 73-91. doi:10.1111/1541-4337.12180Arévalo-Gardini, E., Arévalo-Hernández, C. O., Baligar, V. C., & He, Z. L. (2017). Heavy metal accumulation in leaves and beans of cacao (Theobroma cacao L.) in major cacao growing regions in Peru. Science of The Total Environment, 605-606, 792-800. doi:10.1016/j.scitotenv.2017.06.122Assa, A., Noor, A., Yunus, M. R., Misnawi, & Djide, M. N. (2018). 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Application of Novel Thermal Technology in Foods Processing

Advanced and novel thermal technologies, such as ohmic heating, dielectric heating (e.g., microwave heating and radio frequency heating), and inductive heating, have been developed to improve the effectiveness of heat processing whilst guaranteeing food safety and eliminating undesirable impacts on the organoleptic and nutritional properties of foods. Novel thermal technologies rely on heat generation directly inside foods, which has implications for improving the overall energy efficiency of the heating process itself. The use of novel thermal technologies is dependent on the complexity and inherent properties of the food materials of interest (e.g., thermal conductivity, electrical resistance, water content, pH, rheological properties, food porosity, and presence of particulates). Moreover, there is a need to address the combined use of thermal processing with emerging technologies such as pulsed electric fields, high hydrostatic pressure, and ultrasound to complement the conventional thermal processing of fluid or solid foods. This Special Issue provides readers with an overview of the latest applications of various novel technologies in food processing. A total of eight cutting-edge original research papers and one comprehensive review paper discussing novel processing technologies from the perspectives of food safety, sustainability, process engineering, (bio)chemical changes, health, nutrition, sensory issues, and consumers are covered in this Special Issue

Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?

This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.This research was funded by “BisOlive: Use of olive pomace in the feeding of Bísaro swine. Evaluation of the effect on meat quality” project. NORTE-01-0247-FEDER-072234. Financial support under the CIMO project (UIDB/00690/2020).info:eu-repo/semantics/publishedVersio