Recent Advances in Data Mining Techniques and Their Applications in Hyperspectral Image Processing for the Food Industry

Hyperspectral imaging (HSI) facilitates better characterization of intrinsic and extrinsic properties of foods by integrating traditional spectral and image techniques, in which careful and sophisticated data processing plays an important role. In the past decade, much progress has been made on applying various algorithms to deal with hyperspectral images. This review first introduces the general procedure of hyperspectral data analysis and then illustrates the most typically and commonly used algorithms for denoising, feature selection, model establishment, and evaluation, as well as their applications for assessing food quality, safety, and authenticity. Finally, brief summaries for regression and classification methods are presented. This article will provide a guideline for data mining in the future development of HSI in the food field.     

Principles of Hyperspectral Microscope Imaging Techniques and Their Applications in Food Quality and Safety Detection: A Review

Hyperspectral imaging (HSI) techniques play an important role in the food industry for providing rapid, nondestructive, and chemical‐free detection method, whereas a microscope can provide detailed information about the microstructure of a food item. As an emerging imaging spectroscopy technique, hyperspectral microscope imaging (HMI) technique combines the advantages of HSI with microscopic imaging and has been gradually applied in the food industry. This review introduces the principles of different kinds of HMI techniques, such as fluorescence HMI, visible/near‐infrared HMI, Raman HMI, and infrared HMI. Moreover, detailed applications of HMI techniques are summarized, including evaluation of structures of nutrients, and detection of microorganisms and residues. On the other hand, some challenges and future trends in the applications of these techniques are also discussed. It is concluded that by integrating HSI with microscopy, HMI can not only provide both spectral and spatial information about food substances but also provide their chemical information at the molecular or cellular level. Therefore, HMI techniques have great potentials in nondestructive evaluation of structures of nutrients, and detection of microorganisms and residues for the food industry.     

Recent Advances in De‐Noising Methods and Their Applications in Hyperspectral Image Processing for the Food Industry

There is great interest in developing hyperspectral imaging (HSI) techniques for rapid and nondestructive inspection of food quality, safety, and authenticity. In recent years, image quality has been constantly improved through advances in instrumentation, particularly in more powerful detectors. Nevertheless, pretreatment of data by de‐noising is a necessary step to insure clean HSI datasets for further analysis. This review first introduces the typical and commonly used de‐noising methods in HSI that correct for undesirable variations and remove noisy variables. Their advantages, disadvantages, and implementation are also discussed by giving examples of recent applications in the food industry. Finally, some advice is given for selecting the de‐noising methods that are best suited for a particular application. This review offers an overview of the most frequently applied methods and the latest progress made in HSI de‐noising in food applications. It provides systematic insight into future trends for generating high‐accuracy predictions regarding food safety and quality.     

一种基于高光谱图像的熟牛肉TVB-N含量预测方法

传统肉制品新鲜度检测方法具有耗时费力、效率低、有损等缺陷,提出利用高光谱成像(HSI)技术预测熟牛肉新鲜度指标挥发性盐基氮(TVB-N)含量。首先通过HSI系统获取熟牛肉样本的高光谱数据,并进行黑白校正。进而采用移动平均平滑和多元散射校正对高光谱数据进行预处理。最后采用支持向量回归(SVR)方法分别建立基于全光谱特征、单一光谱特征、单一纹理特征、主成分分析(PCA)融合特征对TVB-N含量的预测模型。结果显示,使用PCA融合特征的SVR模型,对新鲜度的关键指标TVB-N含量的平均预测准确度(APA)可达到85.13%,表明高光谱成像技术与信息融合技术相结合能够提升模型准确度。     

Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry

During the past decade, hyperspectral imaging (HSI) has been rapidly developing and widely applied in the food industry by virtue of the use of chemometric techniques in which wavelength selection methods play an important role. This paper is a review of such variable selection methods and their limitations, describing the basic taxonomy of the methods and their respective advantages and disadvantages. Special attention is paid to recent developments in wavelength selection techniques for HSI in the field of food quality and safety evaluations. Typical and commonly used methods in HSI, such as partial least squares regression, stepwise regression and spectrum analysis, are described in detail. Some sophisticated methods, such as successive projections algorithm, uninformative variable elimination, simulated annealing, artificial neural network and genetic algorithm methods, are also discussed. Finally, new methods not currently used but that could have substantial impact on the field are presented. In short, this review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems.     

Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review

The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research.     

Recent developments and applications of hyperspectral imaging for rapid detection of mycotoxins and mycotoxigenic fungi in food products

Mycotoxins are the foremost naturally occurring contaminants of food products such as corn, peanuts, tree nuts, and wheat. As the secondary metabolites, mycotoxins are mainly synthesized by many species of the genera Aspergillus, Fusarium and Penicillium, and are considered highly toxic and carcinogenic to humans and animals. Most mycotoxins are detected and quantified by analytical chemistry-based methods. While mycotoxigenic fungi are usually identified and quantified by biological methods. However, these methods are time-consuming, laborious, costly, and inconsistent because of the variability of the grain-sampling process. It is desirable to develop rapid, non-destructive and efficient methods that objectively measure and evaluate mycotoxins and mycotoxigenic fungi in food. In recent years, some spectroscopy-based technologies such as hyperspectral imaging (HSI), Raman spectroscopy, and Fourier transform infrared spectroscopy have been extensively investigated for their potential use as tools for the detection, classification, and sorting of mycotoxins and toxigenic fungal contaminants in food. HSI integrates both spatial and spectral information for every pixel in an image, making it suitable for rapid detection of large quantities of samples and more heterogeneous samples and for in-line sorting in the food industry. In order to track the latest research developments in HSI, this paper gives a brief overview of the theories and fundamentals behind the technology and discusses its applications in the field of rapid detection and sorting of mycotoxins and toxigenic fungi in food products. Additionally, advantages and disadvantages of HSI are compared, and its potential use in commercial applications is reported.     

Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation

Hyperspectral imaging (HSI) system in tandem with chemometric methods is proposed as a rapid, efficient, cost‐saving, and nondestructive detection technique, and multivariate data analysis is an indispensable part of this novel detection technique. In recent years, the rapid progress that we have made in using all kinds of chemometric methods to deal with hyperspectral data of meat products, however, cannot meet the practical needs very well. Thus, in order to give some suggestions on how to select an appropriate algorithm for hyperspectral data analysis, this review, first, briefly introduces the principle of the most widely used regression algorithms, and, more importantly, then focuses on the application of different algorithms in modeling the correlation between the quality attributes of the tested sample and their hyperspectral data. The advantages and limitations of these algorithms are compared and discussed. This review article will provide valuable guidelines for data analysis in the future progress of HSI detection technique.     

Multispectral Imaging for Plant Food Quality Analysis and Visualization

The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.     

Recent Developments and Applications of Hyperspectral Imaging for Quality Evaluation of Agricultural Products: A Review

Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious, and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. HSI allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of HSI are also reported.     

Applications of hyperspectral imaging in grains and nuts quality and safety assessment: a review

Quality of foods is generally controlled with traditional methods such as microbiological and chemical tests. However, the necessity of a non-destructive, rapid and accurate on-line method to monitor the product quality and safety is the key topic of many research studies. Hyperspectral imaging (HSI) has emerged as a powerful tool to handle the afore-mentioned goals. It is a novel technique that combines simultaneous advantages of imaging and spectroscopy. HSI is an analytical method that simultaneously delivers chemical, structural and functional information from the sample. This technique can be used to analyze both individual kernels and bulk samples and simultaneously determine quality parameters of grains and nuts. Nuts and grains are nutrient dense foods with complex matrices rich in mono- and poly-unsaturated fatty acids, vegetable proteins, fiber, vitamins, minerals etc. Therefore, nuts and grains are useful dietary sources to decrease the risk of diabetes, cancer and cardiovascular disease. In this paper, recent applications of hyperspectral imaging in quality and safety inspection of nuts and grains such as classification, compositions prediction, texture analysis, and detection of varietal impurities, damages, and infections are reviewed.     

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Fourier transform infrared (FT‐IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT‐IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy‐based FT‐IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.     

高光谱成像技术在食品品质无损检测中的应用

高光谱成像技术结合光谱技术和图像技术的优势,能够很好的捕获光谱信息和图像信息。其丰富的光谱信息能够有效的提取样品内部特征。该分析技术已广泛应用于检测食品的水分含量、新鲜度、生物污染等。本文介绍并分析了高光谱成像技术在食品检测中的各种应用,指出了目前高光谱成像技术存在的不足之处,指明了该技术领域工作者今后的重点研究方向,并对高光谱成像技术发展前景进行了分析和展望。      

高光谱成像技术应用于畜禽肉品品质研究进展

食品安全不光关乎我们身体健康而且影响着社会稳定,做好食品安全检测是防止有毒食品进入人体的关键步骤也是维护社会稳定的重要举措。为改善传统畜禽肉质检测方法费时费力的现状,食品安全检测方法正趋于多样化,引入了许多高新技术,高光谱成像技术（hyperspectral imaging,HSI）便是其中之一。它是将成像技术和光谱技术相结合,在不损伤产品外形的情况下得到产品外部图像特征和产品内部品质信息光谱图,本文从畜禽肉品品质安全角度出发,围绕畜禽肉品化学指标、物理属性和食用安全指标三个方面,综述了高光谱成像技术在畜禽肉品安全检测中的应用,归纳总结了预处理,变量筛选和建模方法。对目前高光谱成像技术应用于畜禽肉品检测中所暴露的缺点进行总结, 并就未来发展方向进行展望,为食品安全检测提供了新的参考。     

Locating calories: Does the high-calorie bias in human spatial memory influence how we navigate the modern food environment?

Human memory appears to be adaptively “biased” towards remembering the locations of (fitness-relevant) high-calorie nutritional resources. It remains to be investigated whether this high-calorie bias in human spatial memory influences how individuals navigate the modern food environment, and whether it is proximally associated with attentional processes. 60 individuals completed computer-based food eye-tracking and spatial memory tasks in a lab setting, as well as a food search and covert food choice task in an unfamiliar supermarket. The high-calorie spatial memory bias was replicated, as individuals more accurately recalled locations of high-calorie relative to low-calorie foods, regardless of hedonic evaluations or familiarity with foods. Although individuals were faster at (re)locating high-calorie (versus low-calorie) items in the supermarket, the bias did not predict a lower search time for high-calorie foods, or a higher proportion of high-calorie food choice. Rather, an enhanced memory for high-calorie food locations was associated with a lower perceived difficulty (i.e. greater ease) of finding high-calorie items in the supermarket, which may potentiate later choice of a high-calorie food. The high-calorie spatial memory bias was also found to be expressed independently of the amount of visual attention individuals allocated to high-calorie versus low-calorie foods. Findings further substantiate the notion that human spatial memory shows sensitivity to the caloric content of a potential resource and automatically prioritizes those with greater energy payoffs. Such a spatial mechanism that was adaptive for energy-efficient foraging within fluctuating ancestral food environments could presently yield maladaptive “obesogenic” consequences, through altering perceptions of food search convenience.     

Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology

The study reports a novel colorimetric sensor array (CSA) based hyperspectral imaging (HSI) system and chemometrics algorithms for the identification of rice storage time. CSA fabricated by boron-dipyrromethene (BODIPY) dyes was used to capture the volatile organic compounds (VOCs) of rice samples. CSA hypercube before and after the reaction were obtained with HSI. Genetic synergy interval partial least square algorithm (GA-Si-PLS) was used to filter spectral information. Fifty-four spectral data variables and five dominant wavelength images was selected from CSA hypercube. Then three grayscale difference values were extracted from each dominant wavelength image, thus totaling to 15 variables as imaging data variables. Linear discriminant analysis (LDA) and k-Nearest Neighbor (KNN) model were established to comparing the performance of spectral variables, imaging variables and combined datasets. The result showed the optimal model was linear discriminant analysis (LDA) model built by using spectral variables and the correct rate of calibration set for rice storage time discrimination was 92.73% and the obtained rate of prediction set was 90.91%. It is indicated the applicability of the proposed CSA combined with HSI technology towards rice storage time identification.     

高光谱成像在食品质量评估方面的研究进展与应用(一)

近年来频发的国内食品质量与安全问题受到全社会的高度重视。因此,需要采用现代化检测技术快速、准确获取食品品质和安全信息。高光谱成像技术是一项将光谱与成像科学相结合的光学分析技术。通过同时获取食品的光谱和空间图像信息,高光谱成像技术能够快速、无损获取食品的品质信息及其空间分布,从而实现食品内外部品质信息的全方位检测,因此在食品安全检测应用领域具有巨大潜力。本文介绍了高光谱成像技术的基本原理,并综述了其在肉类品质检验方面的研究进展。     

A Review of Hyperspectral Imaging for Chicken Meat Safety and Quality Evaluation: Application,Hardware, and Software

Food quality and safety issues have received widespread attention around the world. Traditional analytical methods are cumbersome, time consuming, and disruptive. Consumers and businesses are in desperate need of a fast, nondestructive test to evaluate the safety and quality of food. Chicken is an important food source for the human diet and has a high consumption rate. Its quality and safety issues are especially important. The hyperspectral imaging (HSI) technique combines the main characteristics of the spectroscopy technique and the imaging technique to achieve fast, nondestructive testing and demonstrates great potential for evaluating the food safety and quality of chicken. For the past few years, there have been many studies on the HSI technique for the detection and evaluation of chicken meat safety and quality. Therefore, the purpose of this article is to provide a detailed overview of the HSI technique for microbiological safety detection and quality attribute assessments of chicken meat. In addition, the hardware and software used in the HSI systems are also summarized and compared. Finally, some opinions on the focus of future research and its applications in the modern poultry industry are presented.     

Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical‐free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.     

高光谱成像技术在动物源性食品微生物污染快速检测中的应用进展

高光谱成像(hyperspectral imaging,HSI)技术作为一种无损、快速、准确的检测技术在动物源性食品微生物污染检测方面得到了广泛应用.该技术集图像与光谱技术的优势于一体,可同时检测实验样品的物理特征与化学特征.本文系统地综述HSI原理,及其在动物源性食品微生物(菌落总数、腐败菌、致病菌)污染无损检测方面...