Freezing process evaluation using a portable forced air tunnel with air evacuation and air blowing in pallets

Orientador: Vivaldo Silveira JuniorDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: A redução do tempo de congelamento de produtos alimentícios é um objetivo almejado, devido este processo ser caro e que envolve elevado gasto energético. Os produtos alimentícios são predominantemente congelados em túneis com convecção forçada por insuflação de ar; porém, é preferível realizar a exaustão de ar, ao invés de insuflação, pelo fato da exaustão promover uma circulação de ar mais uniforme ao redor do produto. Um túnel de ar forçado por exaustão é composto por um ventilador que circula de forma a retirar o ar, produzindo uma região de baixa pressão, onde se localiza o produto, buscando uniformizar a circulação do ar frio em contato com o mesmo. Desse modo, este trabalho se propôs à montagem experimental de um túnel ¿portátil¿ de congelamento por ar forçado, onde se pode estudar a utilização de insuflação e exaustão de ar. Este túnel foi construído e alocado no interior de uma câmara de armazenagem de produtos congelados, buscando melhorar a distribuição do ar, potencializando a troca térmica entre o ar e o produto a ser congelado. A montagem foi monitorada através de termopares para determinação das curvas de congelamento e eficiência do sistema. Durante o processo de congelamento, foi avaliada a transferência de calor através da montagem, comparando os processos de exaustão e insuflação e analisados os coeficientes de transferência de calor entre o ar de resfriamento e o produto em diferentes posições e camadas do palete, bem como a distribuição do ar de resfriamento em circulação ao redor do produto. Os resultados mostraram uma redução no tempo de congelamento das amostras com a utilização do túnel ¿portátil¿ em relação ao processo sem a utilização deste aparato dentro da câmara. O processo de exaustão apresentou uma redução de até quatro horas para o congelamento em relação à insuflação. Os valores de coeficiente de convecção foram maiores para a exaustão do que para a insuflação em todas as camadas da montagem, com exceção da superior, que recebia o ar diretamente do interior da câmara na insuflação. Um coeficiente de heterogeneidade foi proposto para avaliar a diferença de temperaturas no produto durante o congelamento. Estes valores, juntamente com a análise das temperaturas obtidas no processo, mostraram que a distribuição do ar, bem como a transferência de calor, ocorre de maneira mais homogênea no interior do palete na exaustão do que na insuflaçãoAbstract: The conditions necessary to keep the air temperature and movement at the product surface will determine the freezing process efficiency. Since the energy level could implement on the final cost, the reduction of the freezing process time was a major goal in the whole experiment. Food products are predominantly frozen in air blast freezing tunnels. Therefore, exhausting air is preferable than blowing it through, since it minimizes air short-circuiting and results in more uniform cooling. To produce an homogeneous refrigeration, the main configuration to be determined for pallets storing plastic packages in boxes are the package distribution and air orientation. The main components of an exhausting forced-air tunnel are a fan that causes the equipment inside air evacuation creating a low pressure region. The product is arranged on this spot creating uniform distribution of the cold air inside the equipment, around the product. Therefore, the objective of this work is to build an experimental portable forced-air freezing tunnel, and work on comparative studies with air exhausting and blowing. The tunnel was built and placed inside a freezing product storage chamber, and the objective was to improve the air circulation and the thermal distribution between the product and cold air, for a sample batch left inside the chamber. The batch was monitored using thermocouples for freezing variation and system efficiency graphic determination. It was also provided the heat transfer analysis, comparing the exhausting and blowing process, and the heat transfer coefficients of the cold air and the product as well as the air distribution around the product. The results have shown reduction of the freezing time of the samples when the portable tunnel was used comparing without the tunnel tests (reference). The air evacuation process reduced up to four hours with comparison to the blowing system the freezing process. Convective coefficient results were higher for air evacuation than air blowing in every part of the batch, except for the upper layer of products were the cold air of the chamber was directly in contact with the product. These results, with the temperature analysis obtained, indicated that the air distribution occurs more uniformly around the products in the exhausting process than blowing system, as well as the heat transferMestradoMestre em Engenharia de Alimento

Computer vision classification of barley flour based on spatial pyramid partition ensemble

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples' classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification1913CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ420562/2018-

Data reduction by randomization subsampling for the study of large hyperspectral datasets

Large amount of information in hyperspectral images (HSI) generally makes their analysis (e.g., principal component analysis, PCA) time consuming and often requires a lot of random access memory (RAM) and high computing power. This is particularly problematic for analysis of large images, containing millions of pixels, which can be created by augmenting series of single images (e.g., in time series analysis). This tutorial explores how data reduction can be used to analyze time series hyperspectral images much faster without losing crucial analytical information. Two of the most common data reduction methods have been chosen from the recent research. The first one uses a simple randomization method called randomized sub-sampling PCA (RSPCA). The second implies a more robust randomization method based on local-rank approximations (rPCA). This manuscript exposes the major benefits and drawbacks of both methods with the spirit of being as didactical as possible for a reader. A comprehensive comparison is made considering the amount of information retained by the PCA models at different compression degrees and the performance time. Extrapolation is also made to the case where the effect of time and any other factor are to be studied simultaneously.J.P Cruz-Tirado acknowledges scholarship funding from FAPESP, grant number 2020/09198–1

Identification of Copper in Stems and Roots of Jatropha curcas L. by Hyperspectral Imaging

The in situ determination of metals in plants used for phytoremediation is still a challenge that must be overcome to control the plant stress over time due to metals uptake as well as to quantify the concentration of these metals in the biomass for further potential applications. In this exploratory study, we acquired hyperspectral images in the visible/near infrared regions of dried and ground stems and roots of Jatropha curcas L. to which different amounts of copper (Cu) were added. The spectral information was extracted from the images to build classification models based on the concentration of Cu. Optimum wavelengths were selected from the peaks and valleys showed in the loadings plots resulting from principal component analysis, thus reducing the number of spectral variables. Linear discriminant analysis was subsequently performed using these optimum wavelengths. It was possible to differentiate samples without addition of copper from samples with low (0.5–1% wt.) and high (5% wt.) amounts of copper (83.93% accuracy, >0.70 sensitivity and specificity). This technique could be used after enhancing prediction models with a higher amount of samples and after determining the potential interference of other compounds present in plants.University of Seville VIPPIT-2019-I.5University of Seville VIPPIT-2019-I.

Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview

AbstractDuring the last two decades, near and mid-infrared spectral analyses have emerged as a reliable and promising analytical tool for objective assessment of coffee quality attributes. The literature presented in this review clearly reveals that near and mid-infrared approaches have a huge potential for gaining rapid information about the chemical composition and related properties of coffee. In addition to its ability for effectively quantifying and characterising quality attributes of some important features of coffee such as moisture, lipids and caffeine content, classification into quality grades and determination of sensory attributes, it is able to measure multiple chemical constituents simultaneously avoiding extensive sample preparation. Developing a quality evaluation system based on infrared spectral information to assess the coffee quality parameters and to ensure its authentication would bring economical benefits to the coffee industry by increasing consumer confidence in the quality of products. This paper provides an overview of the recently developed approaches and latest research carried out in near and mid-infrared spectral technology for evaluating the quality and composition of coffee and the possibility of its widespread deployment

Chia (Salvia hispanica) seeds degradation studied by fuzzy-c mean (FCM) and hyperspectral imaging and chemometrics - fatty acids quantification

Chia seeds are nutritious food because they have a high content of protein, polyunsaturated fatty acids (omega-3 and omega-6) and phenolic compounds. During storage, fatty acids are degraded, by oxidative and hydrolytic reactions, forming free fatty acids (FFA). In this work, we used Near Infrared Hyperspectral Imaging (NIR- HSI) and chemometrics to predict FFA acid value and fatty acids concentrations in chia seeds during storage. First, we explore the hyperspectral images by Fuzzy c-means (FCM), where it is possible to observe as chemical compounds are formed or degraded during storage. Second, PLSR models were developed to predict FFA value and fatty acids concentration. RPD values reached values higher then 2.0, indicating a good ability to estimate these chemical compounds, especially polyunsaturated fatty acids omega-3 and omega-6. Finally, NIR-hyperspectral imaging coupled with chemometrics allowed us to show the chemical degradation process of chia seeds during storage, mainly associated with polyunsaturated fatty acids degradation. Besides NIR-HSI showed to be a powerful technique to quantify the main fatty acids with high accuracy

Determination of pectin content in orange peels by near infrared hyperspectral imaging

Pectin has several purposes in the food and pharmaceutical industry making its quantification important for further extraction. Current techniques for pectin quantification require its extraction using chemicals and pro- ducing residues. Determination of pectin content in orange peels was investigated using near infrared hyper- spectral imaging (NIR-HSI). Hyperspectral images from orange peel (140 samples) with different amounts of pectin were acquired in the range of 900–2500 nm, and the spectra was used for calibration models using multivariate statistical analyses. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed better results considering three groups: low (0–5%), intermediate (10–40%) and high (50–100%) pectin content. Partial least squares regression (PLSR) models based on full spectra showed higher precision (R2 > 0.93) than those based on few selected wavelengths (R2 between 0.92 and 0.94). 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) – Finance Code 00

Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta

[EN] Pasta is mostly composed by wheat flour and water. Nevertheless, flour can be partially replaced by fibers to provide extra nutrients in the diet. However, fiber can affect the technological quality of pasta if not properly distributed. Usually, determinations of parameters in pasta are destructive and time-consuming. The use of Near Infrared-Hyperspectral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the efficiency in the assessment of pasta quality. This work aimed to investigate the ability of NIR-HSI and augmented Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the evaluation, resolution and quantification of fiber distribution in enriched pasta. Results showed R2V between 0.28 and 0.89, %LOF < 6%, variance explained over 99%, and similarity between pure and recovered spectra over 96% and 98% in models using pure flour and control as initial estimates, respectively, demonstrating the applicability of NIR-HSI and MCR-ALS in the identification of fiber in pasta.This work was supported by the Coordenaçao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [Finance Code 001]; Sao Paulo Research Foundation (FAPESP) [grant numbers 2008/57808-1, 2014/50951-4, 2015/24351-2, 2017/17628-3, 2019/06842-0]; and by GVA-IVIA and FEDER funds through project IVIA-51918. The authors would like to thank Nutrassim Food Ingredients company for the donation of the fiber samples, the support provided by Mrs. Cristiane Vidal during NIR-HSI system operation and data processing and Dr. Celio Pasquini for promptly receiving us in the laboratory that he coordinates (Grupo de instrumentaçao e automaçao em quimica analitica, Instituto de quimica, Universidade Estadual de Campinas, Campinas-SP, Brazil) to data acquisition.Teixeira Badaró, A.; Amigo, JM.; Blasco, J.; Aleixos Borrás, MN.; Rios Ferreira, A.; Pedrosa Silva Clerici, MT.; Fernandes Barbin, D. (2021). Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. Food Chemistry. 343:1-9. https://doi.org/10.1016/j.foodchem.2020.128517S1934

Chia (Salvia hispanica) seeds degradation studied by fuzzy-c mean (FCM) and hyperspectral imaging and chemometrics - fatty acids quantification

Chia seeds are nutritious food because they have a high content of protein, polyunsaturated fatty acids (omega-3 and omega-6) and phenolic compounds. During storage, fatty acids are degraded, by oxidative and hydrolytic reactions, forming free fatty acids (FFA). In this work, we used Near Infrared Hyperspectral Imaging (NIR- HSI) and chemometrics to predict FFA acid value and fatty acids concentrations in chia seeds during storage. First, we explore the hyperspectral images by Fuzzy c-means (FCM), where it is possible to observe as chemical compounds are formed or degraded during storage. Second, PLSR models were developed to predict FFA value and fatty acids concentration. RPD values reached values higher then 2.0, indicating a good ability to estimate these chemical compounds, especially polyunsaturated fatty acids omega-3 and omega-6. Finally, NIR-hyperspectral imaging coupled with chemometrics allowed us to show the chemical degradation process of chia seeds during storage, mainly associated with polyunsaturated fatty acids degradation. Besides NIR-HSI showed to be a powerful technique to quantify the main fatty acids with high accuracy