A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Non-destructive imaging and spectroscopic techniques for assessment of carcass and meat quality in sheep and goats: a review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production. © 2020 by the authors.Authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support by national funds FCT/MCTES to CIMO (UIDB/00690/2020); Laboratory of Carcass and Meat Quality of Agriculture School of Polytechnic Institute of Bragança ‘Cantinho do Alfredo’. The authors A. Teixeira and S. Rodrigues are members of the Healthy Meat network, funded by CYTED (ref. 119RT0568). CECAV authors are thankful to the project UIDB/CVT/00772/2020 funded by the Foundation for Science and Technology (FCT, Portugal).info:eu-repo/semantics/publishedVersio

HYPERSPECTRAL IMAGING TECHNIQUE AS A STATE OF ART TECHNOLOGY IN MEAT SCIENCE

Nowadays, the concern of meat consumption, safety and quality has been popular due to some health risks such coronary heart disease, stroke and diabetes caused by the content as saturated fat, cholesterol content and carcinogenic compounds, for consumers. The importance of the need of new non-destructive and fast meat analyze methods are increasing day by day.  For this, researchers have developed some methods to objectively measure the meat quality and meat safety as well as illness sources. Hyperspectral imaging technique is one of the most popular technology which combines imaging and spectroscopic technology. This technique is a non-destructive, real-time and easy-to-use detection tool for meat quality and safety assessment. It is possible to determine the chemical structure and related physical properties of meat. It is clear that hyperspectral imaging technology can be automated for manufacturing in meat industry and all of data’s obtained from the hyperspectral images which represent the chemical quality parameters of meats in the process can be saved to a database.&nbsp

Characterization and identification of poultry meat by non-destructive methods

Orientador: Douglas Fernandes BarbinTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: Atualmente a espectroscopia no infravermelho próximo (NIR) é utilizada na indústria agro-alimentar como uma técnica analítica não destrutiva, por ser rápida e dispensar a utilização de reagentes. No presente estudo, foi utilizada espectroscopia de infravermelho próximo (NIR) com um equipamento portátil e imagens hiperespectrais NIR (NIR-HSI) combinada com algoritmos de aprendizado de máquina e análise multivariada para a classificação e identificação de amostras de carnes moídas. Num primeiro trabalho, foram identificados diferentes partes de frango (peito, sobrecoxa e coxa) . As amostras de diferentes cortes de frango foram classificadas utilizando o NIR portátil combinados com algoritmos de machine learning (ML) e analises multivarida. Atributos físicos e químicos (características de cor, pH e L * a * b *) e composição química (proteína, gordura, umidade e cinzas) foram determinados para cada amostra (moidas e inteiras). Foram utilizados análise de componentes principais (PCA), algoritmos de Suport Vector Machine (SVM) e Random Forest (RF) e análises discriminantes (LDA) para a classificação das amostras. Os resultados confirmaram a possibilidade de diferenciar as amostras de peito, sobrecoxa e coxas com 97% de precisão, comprovando potencial deste método para diferenciar os cortes de frango. Num segundo trabalho, além das tecnologias mencionadas, foi usada a imagem RGB (RGB-I) para classificar três diferentes espécies de carne (frango, suína e bovina) e detectar diferentes quantidades de mistura entre elas. Os dados espectrais foram adquiridos para o NIR portátil no intervalo de comprimento de onda entre 900 e 1700 nm, enquanto para as imagens hiperespectrais no NIR foram entre 900 e 2500 nm. Para a classificação de diferentes espécies de carne moida, realizou-se PCA utilizando-se todas as varivéis e após seleção de variavéis latentes (VL), se realizou a LDA para classificar as amostras puras. Os dados brutos e pré-processados foram investigados separadamente como preditores dos modelos de regressão por mínimos quadrados parciais (PLSR). Além disso, este modelo utilizou as VL mais relevantes, com o objetivo de otimizar o processamento de dados. Os resultados de PLSR foram comparados usando coeficiente de determinação de previsão (R2p), relação do desempenho do desvio (RPD) e razão de intervalo do erro (RER). Os melhores resultados foram com NIR-HSI e RGB-I (R2p = 0,92, RPD = 3,82, RER = 15,77 e R2p = 0,86, RPD = 2,66, RER = 10,99 respectivamente). PCA e LDA aplicadas aos dados espectrais (NIR portátil e NIR-HSI) e nas VL (RGB-I) classificaram os três tipos de carne pura (frango, bovina e suína) com 100% de precisão. Finalmente, conclui-se que essas técnicas têm grande potencial para utilização na indústria de processamento de carnes e por instituições que realizam inspeções de segurança e qualidade dos alimentosAbstract: Near-infrared (NIR) spectroscopy is currently used in the agriculture and food industry as a non-destructive, fast and reagentless analytical technique. In the present study, the use of portable near-infrared (NIR) technology and NIR hyperspectral images combined with machine learning algorithms and multivariate statistical analysis were used to classify samples of different chicken cuts (breast, thigh, and drumstick). In addition to the mentioned technologies, the RGB (RGB-I) image was used to classify three different meat species (chicken, pork and beef) and to detect different amounts of mixture between them. The portable NIR spectral data were acquired in the wavelength range between 900 and 1700 nm, while the hyperspectral images were acquired between 900 and 2500 nm. The different chicken parts were classified using the portable NIR combined with machine learning algorithms (ML) and multivariate analyzes. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample (minced and non-minced). The spectral data exploited by principal component analysis (PCA), the algorithms of support vector machine (SVM) and random forest (RF) and linear discriminant analysis (LDA) were compared for the classification of chicken meat. Results confirmed the possibility of differentiating the breast samples, thighs and drumstick with 97% accuracy. PCA and LDA applied to spectral data (portable NIR and NIR-HSI) and the latent variables (RGB-I) classified 100% of the three types of pure ground meat (chicken, beef, pork). The results showed potential to use NIR portable spectrometer to differentiate the chicken parts and to classify meats of different species together with multivariate analysis. Regarding the classification of different meat species, PCA was performed on all variables and optimized on the latent variables selected with LDA to classify pure samples. Raw and preprocessed data were investigated separately as predictors of Partial Least Squares Regression (PLSR) models. In addition, this model was performed using the most relevant latent variables with the objective of optimizing data processing. Results of PLSR obtained to authenticate the chicken samples with the three spectroscopic techniques were compared using the coefficient of determination for prediction (R2p), ratio performance to deviation (RPD) and ratio of error range (RER). The best results were obtained with NIR-HSI and RGB-I (R2p = 0.92, RPD = 3.82, RER = 15.77 and R2p = 0.86, RPD = 2.66, RER = 10.99 respectively). Based on the results, these techniques can be used on-line by the meat processing industry and by institutions carrying out food safety and quality inspectionsDoutoradoEngenharia de AlimentosDoutora em Engenharia de AlimentosCAPE

Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different Modified Casings

A hyperspectral imaging system was for the first time exploited to estimate the core colour of sausages stuffed in natural hog casings or in two hog casings treated with solutions containing surfactants and lactic acid in slush salt. Yellowness of sausages stuffed in natural hog casings (control group, 20.26 ± 4.81) was significantly higher than that of sausages stuffed in casings modified by submersion for 90 min in a solution containing 1:30 (w/w) soy lecithin:distilled water, 2.5% wt. soy oil, and 21 mL lactic acid per kg NaCl (17.66 ± 2.89) (p < 0.05). When predicting the lightness and redness of the sausage core, a partial least squares regression model developed from spectra pre-treated with a second derivative showed calibration coefficients of determination (Rc2) of 0.73 and 0.76, respectively. Ten, ten, and seven wavelengths were selected as the important optimal wavelengths for lightness, redness, and yellowness, respectively. Those wavelengths provide meaningful information for developing a simple, cost-effective multispectral system to rapidly differentiate sausages based on their core colour. According to the canonical discriminant analysis, lightness possessed the highest discriminant power with which to differentiate sausages stuffed in different casings.Japan Society for the Promotion of Science P16104, 16F16104, 20K1547

Multimode Hyperspectral Imaging for Food Quality and Safety

Food safety and quality are becoming progressively important, and a failure to implement monitoring processes and identify anomalies in composition, production, and distribution can lead to severe financial and customer health damages. If consumers were uncertain about food safety and quality, the impact could be profound; hence, we need better ways of minimizing such risks. On the data management side, the rise of artificial intelligence, data analytics, the Internet of Things, and blockchain all provide enormous opportunities for supply chain management and liability management, but the impact of any approach starts with the quality of the relevant data. Here, we present state-of-the-art spectroscopic technologies including hyperspectral reflectance, fluorescence imaging as well as Raman spectroscopy, and speckle imaging that are all validated for food safety and quality applications. We believe a multimode approach comprising of a number of these synergetic optical detection modes is needed for the highest performance. We present a plan where our implementations reflect this concept through a multimode tabletop system in the sense that a large, real-time production-level device would be based on more modes than this mid-level one, while a handheld, portable unit may only address fewer challenges, but with a lower cost and size