Applications of Hyperspectral Imaging in Chicken Meat Safety and Quality Detection and Evaluation: A Review

Currently, the issue of food safety and quality is a great public concern. In order to satisfy the demands of consumers and obtain superior food qualities, non-destructive and fast methods are required for quality evaluation. As one of these methods, hyperspectral imaging (HSI) technique has emerged as a smart and promising analytical tool for quality evaluation purposes and has attracted much interest in non-destructive analysis of different food products. With the main advantage of combining both spectroscopy technique and imaging technique, HSI technique shows a convinced attitude to detect and evaluate chicken meat quality objectively. Moreover, developing a quality evaluation system based on HSI technology would bring economic benefits to the chicken meat industry. Therefore, in recent years, many studies have been conducted on using HSI technology for the safety and quality detection and evaluation of chicken meat. The aim of this review is thus to give a detailed overview about HSI and focus on the recently developed methods exerted in HSI technology developed for microbiological spoilage detection and quality classification of chicken meat. Moreover, the usefulness of HSI technique for detecting fecal contamination and bone fragments of chicken carcasses are presented. Finally, some viewpoints on its future research and applicability in the modern poultry industry are proposed.     

Emerging nondestructive approaches for meat quality and safety evaluation—A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.     

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。     

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.     

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial,fungal, and viral contaminants

Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf-stable food products. The conventional culture-based methods for microbial detection are time and labor-intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.     

Application of hyperspectral imaging in food safety inspection and control: a review

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.     

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.     

光谱技术在生鲜肉品质安全快速检测的研究进展

光谱技术作为无损快速检测技术在肉品行业中得到广泛应用.该技术能实现生鲜肉快速、在线、准确、无损检测,是各类生鲜肉品质安全分析的重要技术之一.文章综述了近红外光谱、拉曼光谱、高光谱成像技术、荧光光谱等光谱技术在生鲜肉品质检测和安全评定上的重要应用和研究进展.主要包括水分、蛋白质及脂肪等影响肉类营养品质的组成成分分析,肉品食用品质如嫩度、大理石花纹、肉色及新鲜度等指标的评价,肉品加工品质如保水性并由此实行肉类分级的检测以及生鲜肉在微生物污染等安全品质的评定.同时分析各种光谱技术的现状提出存在的问题,并针对目前发展趋势展望了该技术的前景:光谱技术通过与机器视觉技术等新型无损检测技术的有机融合,将实现在线检测评价生鲜肉品质安全的目标.     

Spectral Detection Techniques for Non-Destructively Monitoring the Quality,Safety, and Classification of Fresh Red Meat

Red meat is an important source of nutrients and plays a significant role in human diet. With the development of people’s living standard and relative change of dietary structure in recent years, people propose more requirements for meat. Quality, safety, and classification are three crucial themes related with meat and they are important issues for consumers, retailers, as well as the whole meat industry. However, most of the traditional analytical methods for meat evaluation are time-consuming, laborious, tedious, and destructive, which make them inappropriate for fast analysis and early detection, especially under fast-paced production and processing environment. In contrast to conventional approaches, spectral techniques including near infrared spectroscopy (NIRS), hyperspectral imaging (HSI), and Raman spectroscopy (RS) have emerged and considered as promising tools for meat assessment. The innovative optical sensing techniques can facilitate simple, fast, accurate, and simultaneous measurements of multiple meat attributes. Recently, these techniques have achieved rapid development and attracted more attention of the public. Hence, the goal of this article is to give an overview of the current progress of the spectral techniques for evaluation of fresh red meat (pork, beef, and lamb). The spectral techniques are described in terms of their basic working principle, fundamental configurations, analysis process, as well as applications on meat inspection. In addition, the problems to be tackled and future potential trends of these spectral methods are also discussed in this paper.     

Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review

With improvement in people's living standards, many people nowadays pay more attention to quality and safety of meat. However, traditional methods for meat quality and safety detection and evaluation, such as manual inspection, mechanical methods, and chemical methods, are tedious, time-consuming, and destructive, which cannot meet the requirements of modern meat industry. Therefore, seeking out rapid, non-destructive, and accurate inspection techniques is important for the meat industry. In recent years, a number of novel and noninvasive imaging techniques, such as optical imaging, ultrasound imaging, tomographic imaging, thermal imaging, and odor imaging, have emerged and shown great potential in quality and safety assessment. In this paper, a detailed overview of advanced applications of these emerging imaging techniques for quality and safety assessment of different types of meat (pork, beef, lamb, chicken, and fish) is presented. In addition, advantages and disadvantages of each imaging technique are also summarized. Finally, future trends for these emerging imaging techniques are discussed, including integration of multiple imaging techniques, cost reduction, and developing powerful image-processing algorithms.     

近红外光谱分析技术在鸡肉品质检测中的应用研究进展

全球的鸡肉产量和消费量逐年上升,鸡肉品质已成了消费者的重点关注对象。近红外光谱分析技术是一种无损、快速、环保以及实时的检测技术,目前已快速发展成为一种应用于食品分析领域的新方法,在肉品检测方面也展现出极大的分析和应用潜力,近年来,已有较多国内外研究探讨了其在鸡肉品质检测方面的可行性。本文通过对近红外检测技术在鸡肉颜色、p H值、持水力、剪切力及化学成分预测方面的定量分析,肉品动物来源、鸡肉产地溯源及品质等级划分的定性分析以及其他相关方面检测的研究进行了综述,总结了应用近红外检测各品质指标的研究现状,可为相关研究者提供参考和借鉴,并基于分析总结对今后近红外光谱分析在鸡肉品质检测应用的发展前景及发展方向做了展望。     

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.     

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.     

Advances in Nondestructive Methods for Meat Quality and Safety Monitoring

ABSTRACT

Meat is highly perishable and poses health threats when its quality and safety is unmonitored. Chemical methods of quality and safety determination are expensive, time-consuming and lack real-time monitoring applicability. Nondestructive techniques have been reported as antidotes to these constraints. This paper assessed the potential of nondestructive techniques such as near-infrared spectroscopy, hyperspectral imaging, multispectral imaging, e-nose, and their data fusion, all combined with algorithms for quality monitoring of pork, beef, and chicken, the most consumed meat sources in the world. These techniques combined with data processing applications may offer a panacea for real-time industrial meat quality and safety monitoring.     

Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques

Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. This study attempts to quantify the TVC content in pork by combining two nondestructive sensing tools of hyperspectral imaging (HSI) and artificial olfaction system based on the colorimetric sensor array. First, data were acquired using HSI system and colorimetric sensors array, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from two different sensor data for further multivariate analysis. In developing the models, linear (PLS and stepwise MLR) and nonlinear (BPANN and SVMR) pattern recognition methods were comparatively employed, and they were optimized by cross-validation. Compared with other models, the SVMR model achieved the best result, and the optimum results were achieved with the root mean square error of prediction (RMSEP)?=?2.9913 and the determination coefficient (R _p )?=?0.9055 in the prediction set. The overall results showed that it has the potential in nondestructive detection of TVC content in pork meat by integrating two nondestructive sensing tools of HSI and colorimetric sensors with SVMR pattern recognition tool.     

Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

Food quality and safety have become the top priorities for agriculture and food processing industry due to the increasing consumer demand for high-quality healthy food. The food processing industry is currently focusing on using fast, precise, and nondestructive automated quality inspection techniques. Near-infrared spectroscopy, image processing, hyperspectral imaging, X-rays, and ultrasonic techniques have been researched and shown to have high potential for automated inspection. The biggest challenge in the automated inspection systems deals with signal pre-processing, denoising, feature extraction, and its re-synthesis for classification purposes. Several research studies have established that the technique of wavelet analysis can very well resolve these issues of signal processing in many systems used for quality inspection of agricultural and food products. The objective of this paper is to discuss the theory of wavelet analysis and review its application in signal processing and feature extraction for quality monitoring of agricultural and food products.     

低场核磁共振无损检测技术在水产品加工贮藏方面的应用

水分的含量及分布状态对于水产品的品质有重要的影响。低场核磁共振技术因其能快速、无损地检测食品水分含量和及其分布与迁移状况,在水产品加工和贮藏的品质分析方面有巨大应用潜力。核磁弛豫技术可对水产品加工过程水分进行检测、区分和对理化指标进行预测;核磁成像技术可对水产品进行内部水分的可视化观测。本文总结了最近低场核磁共振技术在水产品加工贮藏中的应用,对低场核磁共振作为无损、快速的检测技术在水产品加工和贮藏领域的应用前景进行了展望。     

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。     

高光谱成像技术在肉类安全无损检测的应用研究进展

高光谱成像技术在肉类安全品质预测及分选分级方面已取得了诸多成果。作者重点综述了其在肉类有毒有害物质检测、肉类掺假检测、肉类分选分级中的研究现状,讨论了其存在的不足及发展趋势,以期为肉类安全无损检测方法的研究提供参考。     

Recent Applications of Spectroscopic and Hyperspectral Imaging Techniques with Chemometric Analysis for Rapid Inspection of Microbial Spoilage in Muscle Foods

Muscle food is one of the most perishable food products because of its vulnerability to microbial spoilage, which can result in critical food safety problems. Traditional techniques for detection and evaluation of microbial spoilage in muscle foods are tedious, laborious, destructive, and time‐consuming. In recent years, spectroscopic and imaging technologies have shown great potentials for the assessment of food quality and safety due to their nondestructive, noninvasive, cost‐effective, and rapid responsive nature. This review focuses on the applications of several valuable spectroscopic techniques including visible and near‐infrared spectroscopy, Fourier transform infrared spectroscopy, fluorescence spectroscopy, Raman spectroscopy, and hyperspectral imaging for the rapid and nondestructive detection of microbial spoilage in common muscle foods such as meat, poultry, fish, and related products. Combined with chemometric analysis, such as spectral preprocessing and modeling methods, these potential technologies have been successfully developed for the determination of total viable count, aerobic plate count, Enterobacteriaceae, Pseudomonas, Escherichia coli, and lactic acid bacteria loads in muscle foods. Moreover, the advantages and disadvantages of these techniques are discussed and some perspectives about future trends are also presented.