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Background Most contamination in agricultural and food products (AFP) is caused by undesirable microorganisms and how to detect microbial contaminants in a rapid and reliable way has always been a topic of great interest for researchers. By integrating traditional spectroscopy and digital imaging function into one system, hyperspectral imaging simultaneously providing spectral and spatial information is proposed and could be used as a smart and promising technology for microbial evaluation in AFP. Scope and approach In this review, we summarized the recent progresses and applications of hyperspectral imaging in replacing conventional time-consuming, tedious, labor-intensive and destructive techniques by providing a rapid, real-time, non-destructive and efficient alternative for detecting various microbial contaminants in raw and processed AFP. The great potential and importance of hyperspectral imaging is emphasized and approved with its satisfactory performance in microbial evaluation of various AFP. Key findings and conclusions Although hyperspectral imaging shows some obvious advantages, it still faces challenges in terms of accurate reference value measurement, fast data mining and advanced system development. With the further research and development of optics, it would be expected that efficient multispectral imaging systems would likely be developed and applied for online microbial evaluation in agricultural and food industry. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, hyperspectral imaging has gained a wide recognition as a non-destructive and fast quality and safety analysis and assessment method for a wide range of food products. As the second part of this review, applications in quality and safety determination for food products are presented to illustrate the capability of this technique in the food industry for classification and grading, defect and disease detection, distribution visualization of chemical attributes, and evaluations of overall quality of meat, fish, fruits, vegetables, and other food products. The state of the art of hyperspectral imaging for each of the categories was summarized in the aspects of the investigated quality and safety attributes, the used systems (wavelength range, acquisition mode), the data analysis methods (feature extraction, multivariate calibration, variables selection), and the performance (correlation, error, visualization). With its success in different applications of food quality and safety analysis and assessment, it is evident that hyperspectral imaging can automate a variety of routine inspection tasks. Industrial relevance: It is anticipated that real-time food monitoring systems with this technique can be expected to meet the requirements of the modern industrial control and sorting systems in the near future. [Copyright &y& Elsevier]

Abstract: This study was carried out for rapid and non-invasive measurement of color distribution in salmon fillet using hyperspectral imaging in long-wave near infrared spectral range (LW-NIR). Successive projections algorithm (SPA) was used to select effective wavelengths. Instead of selecting different sets of effective wavelengths for each color component respectively, instrumental effective wavelengths (IEWs) were identified for the prediction of all three color components, leading to reducing the number of band-pass filters for designing the multispectral imaging system. Meanwhile, predictive effective wavelengths (PEW) were further chosen from IEW to optimize calibration models. The final prediction models were considered as the multiple linear regression (MLR) models with PEW, which resulted in correlation coefficients (r C ) of 0.876, 0.744, and 0.803 for L*, a*, and b*, respectively. Finally, the visualization of color distribution in salmon fillets was achieved. The results demonstrated that LW-NIR hyperspectral imaging is a potential technique to quantitatively measure color distribution of salmon fillet in a rapid and non-invasive way. Industrial relevance: This study was conducted to evaluate the feasibility of a hyperspectral imaging system in the LW-NIR spectral region (964 to 1631 nm) for rapid measurement of color in intact salmon fillets. On the basis of the results and accompanying illustrations presented in this work, the study demonstrated the ability of the method based on LW-NIR hyperspectral imaging to measure color distribution of salmon fillets. This method was a rapid, contact-free, and consistent evaluation, and can be used as a reliable and rapid alternative to traditional colorimeter for measuring color of salmon fillet for the food industry. [Copyright &y& Elsevier]

This study aimed to develop an alternative technique for rapid, accurate and non-invasive determination of gross energy density values of salmon fillets based on hyperspectral imaging (900–1700 nm). Spectral data were extracted from the hyperspectral images with outlier pixels removed. Good performances were achieved with r P of 0.906 and 0.909, and normalized RMSEP of 6.768% and 7.064% for partial least squares regression (PLSR) and epsilon-support vector regression (epsilon-SVR) analysis, respectively. The optimized stepwise-PLS model built with four wavelengths (931, 1001, 1135 and 1168 nm) yielded good results with r P of 0.908 and normalized RMSEP of 6.871%. The distribution maps for visualizing energy density indices in all portions of the salmon fillet were subsequently generated. The overall results confirmed the successful development of a rapid and non-destructive technique using hyperspectral imaging for determining energy value of salmon fillets, and the provision of an alternative method for caloric density measurement for the food industry. [ABSTRACT FROM AUTHOR]

There is a great interest in developing optical techniques that have the capability of predicting quality attributes, safety parameters and authenticity in real-time assessment. Recently, hyperspectral imaging technique has been widely used for rapid and non-destructive inspection of various food products. Although the technique is currently in an early development stage, its potential is promising. Due to the extensive time needed for the processing of the large volumes of data, hyperspectral imaging technique cannot be directly implemented in an online system. However, selecting some feature wavelengths from hyperspectral images can be useful to develop a multispectral imaging system, which can meet the speed requirement of industrial production. Indeed, the success of multispectral imaging heavily depends on the effectiveness of hyperspectral imaging (HSI) for providing the feature wavelengths. If the high dimensionality of hyperspectral data can be reduced properly in order to design/form a low-cost multispectral imaging sensor based on some selected feature wavelengths for certain applications, the technique would certainly be incomparable for process monitoring and real-time inspection. This review first introduces the fundamental steps for selecting feature wavelengths from hyperspectral data and then describes the feature wavelengths derived from hyperspectral imaging applications to make a more effective and efficient multispectral real-time imaging system. It is anticipated that this review can act as a basis for researchers and industry for further development of online multispectral inspection system for quality, safety and authenticity of muscles food. [ABSTRACT FROM AUTHOR]