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Background Food quality and safety is of great concern to governments, the food industry, as well as consumers. Thus, research has been conducted to develop advanced detection techniques instead of traditional methods to provide rapid, non-destructive food quality and safety evaluation and analysis for the industry. Scope and approach As an emerging technology, Raman imaging has been successfully studied in food safety assessment and control. This review provides a comprehensive overview on the applications of Raman imaging in the area of food quality evaluation, assessment of adulterants and contaminants, and detection of pesticides. Other relevant techniques are also reported due to their close relationship with food safety control. Conclusions and key findings With these applications, it is evident that Raman imaging has given promising results and thus could be a powerful technique for food quality surveillance as well as for reducing the occurrence of food safety issues. [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]

Computer vision has emerged as a useful alternative to manual expert grading of meat in recent years. Manual grading by experts has a number of essential flaws that can be effectively mitigated with computer vision technology. Computer vision technology is a simple and affordable alternative that, while requiring some expertise, is not excessively technologically demanding. Computer vision technology has shown key attributes of flexibility and ease of compatibility allowing a wide range of meat quality assessment challenges to be successfully tackled. The simplest approaches involving visible light imaging and explicit statistical modelling have proven adequate on the vast majority of occasions. On the other hand, for difficult tasks, more expensive non-visible wavelength imaging and implicit statistical modelling are required. In addition, fully automatic image segmentation still remains a difficult problem in many instances, although image processing is becoming more powerful particularly as computationally demanding texture characterisation algorithms become more viable as computation speeds increase. Finally training and testing a computer vision system will require considerable groundwork as a substantial amount of image and independent meat quality data will be required for good model calibration and validation. [Copyright &y& Elsevier]

With increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables. Computer vision has been successfully adopted for the quality analysis of meat and fish, pizza, cheese, and bread. Likewise grain quality and characteristics have been examined by this technique. This paper presents the significant elements of a computer vision system and emphasises the important aspects of the image processing technique coupled with a review of the most recent developments throughout the food industry. [Copyright &y& Elsevier]

Process analysis is an important step for online food quality control during food processing. Among the emerging non-destructive examination techniques that offer rapid detection, terahertz spectroscopy has attracted attention in analysing food processes based on various quality parameters. Moreover, calibration and chemometric methods are frequently used for spectral data analysis. The system normally consists of processing equipment and terahertz time-domain spectral imaging (THz-TDS), which often exhibits high prediction accuracy combined with appropriate chemometrics methods. Therefore, terahertz spectroscopy has been considered highly suitable for on-site and in-situ process analysis to enhance manufacturing and thus improve product quality. In this review, applications of THz-TDS in food processes of dehydration, storage, freezing, and fermentation, as well as other areas, are introduced and discussed. Among these, terahertz technology has attracted intensive research for process analysis related to moisture changes, such as dehydration and storage under natural drying conditions. Emerging techniques of THz-TDS with other processing techniques, such as hot air drying (HAD) and microwave vacuum dehydration (MVD), are frequently investigated. Applications in other areas, including analysing fermentation, freezing, and hygroscopic processes, were also reported. For freezing analysis, it is mainly used to analyze (i) the effect of repeated freezing and thawing on meat and (ii) the growth rate of ice crystals. In analysing fermentation processing, although most of the research has confirmed the feasibility of THz-TDS, further investigation regarding the accuracy is still awaited. The combination of THz-TDS with calibration and chemometrics has been widely employed in analysing the non-linear changes of quality attributes during food processing with the emergence of numerous related research in recent years. It is highly suitable for analysing moisture content and other polar substances such as hydrogen sulfide and ammonia. Despite such high performance, drawbacks of high cost, interference from high moisture content, and high disturbance of other constituents such as protein are still significant obstacles to the universalization of THz-TDS. • Recent novel research on THz-TDS in food processing analysis was reviewed. • THz-TDS shows the potential for online monitoring of fermentation processes. • Chemometrics and algorithms significantly improve the accuracy of THz-TDS. • Drawbacks and future perspectives of THz-TDS were proposed and discussed. [ABSTRACT FROM AUTHOR]