Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot‐air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.Bundesanstalt für Landwirtschaft und Ernährung http://dx.doi.org/10.13039/501100010771German Research Foundation (DFG‐Deutsche Forschungsgemeinschaft) http://dx.doi.org/10.13039/501100001659Institut Penyelidikan dan Kemajuan Pertanian Malaysia http://dx.doi.org/10.13039/501100007702Federal Ministry of Food and Agriculture http://dx.doi.org/10.13039/501100005908Coordination of European Transnational Research in Organic Food and Farming Systems http://dx.doi.org/10.13039/501100011598the Universität KasselPeer Reviewe

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 Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat

Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast

sensor

Desarrollo de algoritmos matemáticos para detectar la presencia de bacterias, hongos y plagas, utilizando sistemas de procesamiento de imágenes.

A nivel mundial, la preocupación por la seguridad alimentaria se ha convertido en un tema de gran relevancia. En respuesta a esta demanda han emergido los sistemas de procesamiento de imágenes hiperespectrales para la rápida detección de agentes peligrosos en los alimentos. Por lo anterior, este proyecto de investigación se desarrolló un nuevo algoritmo que conjuntamente se utilizó con una cámara hiperespectral PIKE F-210B, un espectrógrafo ImSpector V10E y un sistema de iluminación halógeno-tungsteno sin regulador para la detección con procesamiento de imágenes hiperespectrales de la bacteria Salmonella typhimurium en superficies de tomate dentro del rango espectral de 400–1000 nm. También, se generaron modelos Gaussianos para obtener áreas bajo la curva con integrales definidas, la cual proporciona un enfoque cuantitativo de cada una de las firmas espectrales. Entre la metodología utilizada se incluyó la aplicación de tres dosis (5, 10 y 15 ȝL) y una respuesta de control (0 ȝL) en la superficie de 20 frutos de tomate. Posteriormente, se observó que algunas firmas espectrales disminuyeron su amplitud máxima en las dosis más altas de Salmonella; además, esas firmas espectrales mostraron los valores numéricos más bajos. Y el análisis de varianza de un solo factor no mostró significancia debido a las dosis; sin embargo, se concluyó que el algoritmo proporciona una buena metodología para la detección de éste patógeno. En una segunda investigación se inocularon tres zonas de la superficie de tomate con dosis (5, 10 y 15 ȝL). y de las imágenes hiperespectrales obtenidas se realizó un análisis de componentes principales, donde la componente principal 1 agrupó el 99% de las firmas espectrales El desempeño del sistema de adquisición y procesamiento de imágenes hiperespectrales fue de 650 fotogramas en 5 minutos, confirmando la presencia de la bacteria. En una tercera investigación se desarrolló una plataforma giratoria y un algoritmo para que el sistema de imágenes hiperespectrales pudiera escanear y reconstruir todo el exocarpo de un fruto de tomate. El mecanismo de rotación y el algoritmo desarrollado pueden ser utilizados para futuras investigaciones dirigidas a la cuantificación del porcentaje total de daño por bacterias en la superficie del exocarpo de frutos de tomate. En un cuarto experimento acerca de las aplicaciones del procesamiento de imágenes hiperespectrales para identificar enfermedades, se eligió a Fusarium graminearum ya que tanto en México como en Canadá afecta la producción del cultivo de trigo. El hongo Fusarium provoca pérdidas económicas en ese cultivo y daños a la salud de las personas y animales, porque genera metabolitos secundarios conocidos como micotoxinas, dentro de las cuales la más mortífera es conocida como deoxinivalenol (DON). Actualmente, la detección del daño provocado por esta enfermedad es realizada de manera visual, y conlleva grandes desventajas debido a la naturaleza humana (fatiga durante la inspección, clasificaciones erróneas, tiempos elevados para grandes muestras). Como alternativa, los sistemas de imágenes hiperespectrales, en el espectro visible, surgen como una herramienta para solventar los problemas anteriores. En esta investigación se detectó éste patógeno sobre espigas de trigo sin trillar mediante el uso de análisis de componentes principales, así como el algoritmo conocido como mapeador de ángulo espectral y utilizando un sistema de procesamiento de imágenes hiperespectrales (cámara y un sistema de iluminación con regulador, Specim FX10). El error de clasificación fue menor al 10% para análisis de componentes principales, y 8.6% para el mapeador de ángulo espectral. En el quinto estudio de investigación se desarrolló un sistema de adquisición y procesamiento de imágenes para el conteo y la medición de la dispersión de áfidos de la caña de azúcar también conocidos como pulgones amarillos del sorgo. La infestación y la dispersión del pulgón amarillo causaron daños de entre el 30 y el 100% en la cosecha de sorgo en México. La infestación y la dispersión de este áfido están siendo cuantificadas por personas de manera visual; sin embargo, lleva un tiempo considerable y es una tarea tediosa, por lo cual, se realizó un algoritmo y un código que se ejecutó en la plataforma Imagen J para contar a las ninfas y adultos del pulgón amarillo en imágenes tomadas por un teléfono inteligente. El resultado obtenido con el programa de computadora fue de 267 pulgones en una hoja de sorgo, que fue la misma cantidad contada por la inspección visual de una persona. Además, el índice del vecino más cercano (0.3588) mostró un grado significativo de agrupamiento de los áfidos. Debido a la rápida propagación y reproducción de la plaga en diferentes áreas de México, es esencial utilizar tecnologías como la adquisición y el procesamiento de imágenes, así como la máquina de visión para ayudar a monitorear y acelerar el proceso de conteo. También es muy importante considerar en futuras investigaciones la liberación de una cantidad adecuada de insectos beneficiosos, como el Coccinellidae, debido a que pueden controlar la población del pulgón amarillo en el cultivo de sorgo, así como también el uso del procesamiento de imágenes hiperespectrales. ABSTRACT Worldwide, concern for food security has become a major issue. In response to this demand, hyperspectral imaging systems for the rapid detection of pathogens in food have emerged. Therefore, this research project developed a new algorithm that was jointly used with a hyperspectral camera PIKE F-210B, an ImSpector V10E spectrograph and a halogen-tungsten lighting system without a regulator and using hyperspectral imaging for detection of the Salmonella typhimurium bacteria on tomato surfaces within the spectral range of 400-1000 nm. Also, Gaussian models were generated to obtain areas under the curve with definite integrals, which provides a quantitative approach for each of the spectral signatures. Among the methodology used was the application of three doses (5, 10 and 15 ȝL) and a control response (0 ȝL) on the surface of 20 tomato fruits. Subsequently, it was observed that some spectral signatures decreased their maximum amplitude in the highest doses of Salmonella; In addition, these spectral signatures showed the lowest numerical values. And the analysis of variance of a single factor did not show significance due to the doses; however, it was concluded that the algorithm provides a good methodology for the detection of this pathogen. In a second research, three areas of the tomato surface were inoculated with doses (5, 10 and 15 ȝL). and from the hyperspectral images obtained, a principal components analysis was performed, where the main component 1 grouped 99% of the spectral signatures. The performance of the hyperspectral image acquisition and processing system was 650 frames in 5 minutes, confirming the presence of the bacteria. In a third investigation, a revolving platform and an algorithm were developed so that the hyperspectral imaging system could scan and reconstruct the entire exocarp of a tomato fruit. The mechanism of rotation and the algorithm developed can be used for future research aimed at quantifying the total percentage of damage by bacteria on the surface of the exocarp of tomato fruits. In a fourth experiment about the applications of hyperspectral image processing to identify diseases, Fusarium graminearum was selected since both in Mexico and in Canada it affects the production of the wheat crop. The fungus Fusarium causes economic losses in that crop and damages the health of people and animals, because it generates secondary metabolites known as mycotoxins, among which the deadliest is known as deoxynivalenol (DON). Currently, the detection of damage caused by this disease is performed visually, and involves great disadvantages due to human nature (fatigue during inspection, erroneous classifications, high times for large samples). As an alternative, hyperspectral imaging systems, in the visible spectrum, emerge as a tool to solve the above problems. In this research, this pathogen was detected on unthreshed wheat spikes through the use of principal component analysis, as well as the algorithm known as a spectral angle mapper and using a hyperspectral image processing system (camera and a lighting system with a regulator, Specim FX10). The classification error was less than 10% for principal component analysis, and 8.6% for the spectral angle mapper. In the fifth research study, an image acquisition and processing system was developed for counting and measuring the dispersion of sugarcane aphids also known as yellow sorghum aphids. The yellow aphid infestation and dispersion caused between 30 and 100% damage in the sorghum crop in Mexico. The infestation and dispersal of this aphid are being quantified by people in a visual way; However, it takes a considerable time and is a tedious task, so, an algorithm and a code was executed on the Image J platform to tell the nymphs and adults of the yellow aphid in images taken by a smartphone. The result obtained with the computer program was 267 aphids on a leaf of sorghum, which was the same amount counted by the visual inspection of a person. In addition, the nearest neighbor index (0.3588) showed a significant degree of grouping of aphids. Due to the rapid spread and reproduction of the pest in different areas of Mexico, it is essential to use technologies such as the acquisition and processing of images, as well as the vision machine to assist monitoring and rapid counting process. It is also very important to consider in future research the release of an adequate amount of beneficial insects, such as Coccinellidae, because they can control the yellow aphid population in sorghum culture, as well as the use of hyperspectral image processing