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Nondestructive Optical Sensing for Food Quality and Safety Inspection
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Nondestructive Optical Sensing for Food Quality and Safety Inspection

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 15114

Special Issue Editors

Prof. Dr. Yuzhen Lu

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Interests: optical sensing; food inspection; precision agriculture; plant phenotyping; machine learning; automation
Dr. Seung-Chul Yoon

E-Mail Website
Guest Editor
USDA-ARS Quality & Safety Assessment Research Unit, Athens, GA, USA
Interests: hyperspectral imaging; artificial intelligence; deep learning; real-time machine vision; non-destructive sensing of agricultural and food products for safety and quality assessment; big image data
Special Issues, Collections and Topics in MDPI journals
Dr. Haibo Yao

E-Mail Website
Guest Editor
Research Professor, Geosystems Research Institute, Mississippi State University, Stennis Space Center, Starkville, MS 39529, USA
Interests: remote sensing and engineering solutions for agriculture, post-harvest contamination of aflatoxin, hyperspectral imaging, food safety and contamination detection; algorithm development; instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The food industry continues to be faced with needs to supply safe and high-quality food products while addressing the issue of food loss and waste reduction. Non-destructive optical sensing, such as point spectroscopy and optical imaging, utilizes light to characterize and measure the physical, chemical, and biological properties of food materials, and it promises to meet the industrial needs and ensure consumer satisfaction. Recent advances in artificial intelligence and computing technologies have opened new opportunities for enhanced food quality and safety inspection. However, many technical hurdles still lie ahead for the quantitative characterization of food quality/safety attributes, mining/modelling of large volumes of sensor data, and practical deployment/integration of the optical sensing techniques in production lines. This Special Issue aims to disseminate novel research in the development and application of non-destructive optical sensing techniques for the quality and safety inspection of agricultural and food products. Authors are welcome to submit original research and review papers covering but not limited to the following topics:

  • Instrumentation of innovative optical sensing techniques;
  • Assessment of external and internal quality attributes;
  • Detection of food spoilage and contamination;
  • Quantitative optical characterization and mapping;
  • Hyperspectral and multispectral imaging for food inspection;
  • Sensor fusion for enhanced food inspection;
  • Real-time inspection of food products;
  • Spectroscopic and image data processing methods;
  • Artificial intelligence and robotics for food inspection.
Prof. Dr. Yuzhen Lu
Dr. Seung-Chul Yoon
Dr. Haibo Yao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Vis-NIR-IR Spectroscopy
  • Optical imaging
  • Hyperspectral/multispectral imaging
  • Food quality
  • Food safety
  • Chemometrics
  • Image analysis
  • Machine learning
  • Real-time applications

Published Papers (6 papers)

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Research

12 pages, 4561 KiB  
Article
Rapid and Low-Cost Detection of Millet Quality by Miniature Near-Infrared Spectroscopy and Iteratively Retaining Informative Variables
by Fuxiang Wang, Chunguang Wang and Shiyong Song
Foods 2022, 11(13), 1841; https://doi.org/10.3390/foods11131841 - 22 Jun 2022
Cited by 4 | Viewed by 1448
Abstract
Traditional chemical methods for testing the fat content of millet, a widely consumed grain, are time-consuming and costly. In this study, we developed a low-cost and rapid method for fat detection and quantification in millet. A miniature NIR spectrometer connected to a smartphone [...] Read more.
Traditional chemical methods for testing the fat content of millet, a widely consumed grain, are time-consuming and costly. In this study, we developed a low-cost and rapid method for fat detection and quantification in millet. A miniature NIR spectrometer connected to a smartphone was used to collect spectral data from millet samples of different origins. The standard normal variate (SNV) and first derivative (1D) methods were used to preprocess spectral signals. Variable selection methods, including bootstrapping soft shrinkage (BOSS), the variable iterative space shrinkage approach (VISSA), iteratively retaining informative variables (IRIV), iteratively variable subset optimization (IVSO), and competitive adaptive reweighted sampling (CARS), were used to select characteristic wavelengths. The partial least squares regression (PLSR) algorithm was employed to develop the regression models aimed at predicting the fat content in millet. The results showed that the proposed 1D-IRIV-PLSR model achieved optimal accuracy for fat detection, with a correlation coefficient for prediction (Rp) of 0.953, a root mean square error for prediction (RMSEP) of 0.301 g/100 g, and a residual predictive deviation (RPD) of 3.225, by using only 18 characteristic wavelengths. This result highlights the feasibility of using this low-cost and high-portability assessment tool for millet quality testing, which provides an optional solution for in situ inspection of millet quality in different scenarios, such as production lines or sales stores. Full article
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
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26 pages, 6624 KiB  
Article
HOG-SVM Impurity Detection Method for Chinese Liquor (Baijiu) Based on Adaptive GMM Fusion Frame Difference
by Xiaoshi Shi, Zuoliang Tang, Yihan Wang, Hong Xie and Lijia Xu
Foods 2022, 11(10), 1444; https://doi.org/10.3390/foods11101444 - 17 May 2022
Viewed by 1869
Abstract
Chinese liquor (Baijiu) is one of the four major distilled spirits in the world. At present, liquor products containing impurities still exist on the market, which not only damage corporate image but also endanger consumer health. Due to the production process and packaging [...] Read more.
Chinese liquor (Baijiu) is one of the four major distilled spirits in the world. At present, liquor products containing impurities still exist on the market, which not only damage corporate image but also endanger consumer health. Due to the production process and packaging technologies, impurities usually appear in products of Baijiu before entering the market, such as glass debris, mosquitoes, aluminium scraps, hair, and fibres. In this paper, a novel method for detecting impurities in bottled Baijiu is proposed. Firstly, the region of interest (ROI) is cropped by analysing the histogram projection of the original image to eliminate redundant information. Secondly, to adjust the number of distributions in the Gaussian mixture model (GMM) dynamically, multiple unmatched distributions are removed and distributions with similar means are merged in the process of modelling the GMM background. Then, to adaptively change the learning rates of the front and background pixels, the learning rate of the pixel model is created by combining the frame difference results of the sequence images. Finally, a histogram of oriented gradient (HOG) features of the moving targets is extracted, and the Support Vector Machine (SVM) model is chosen to exclude bubble interference. The experimental results show that this impurity detection method for bottled Baijiu controls the missed rate by within 1% and the false detection rate by around 3% of impurities. Its speed is five times faster than manual inspection and its repeatability index is good, indicating that the overall performance of the proposed method is better than manual inspection with a lamp. This method is not only efficient and fast, but also provides practical, theoretical, and technical support for impurity detection of bottled Baijiu that has broad application prospects. Full article
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
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15 pages, 1436 KiB  
Article
Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques
by Hanim Z. Amanah, Salma Sultana Tunny, Rudiati Evi Masithoh, Myoung-Gun Choung, Kyung-Hwan Kim, Moon S. Kim, Insuck Baek, Wang-Hee Lee and Byoung-Kwan Cho
Foods 2022, 11(2), 232; https://doi.org/10.3390/foods11020232 - 16 Jan 2022
Cited by 14 | Viewed by 2175
Abstract
The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to [...] Read more.
The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model. Full article
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
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14 pages, 5329 KiB  
Article
Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method
by Yifei Zhang, Xuhai Yang, Zhonglei Cai, Shuxiang Fan, Haiyun Zhang, Qian Zhang and Jiangbo Li
Foods 2021, 10(12), 2983; https://doi.org/10.3390/foods10122983 - 03 Dec 2021
Cited by 11 | Viewed by 2062
Abstract
Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with [...] Read more.
Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples. Full article
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
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12 pages, 1877 KiB  
Article
Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis
by Fuyun Wang, Hao Lin, Peiting Xu, Xiakun Bi and Li Sun
Foods 2021, 10(9), 2176; https://doi.org/10.3390/foods10092176 - 14 Sep 2021
Cited by 6 | Viewed by 2626
Abstract
This work presents a novel work for the detection of the freshness of eggs stored at room temperature and refrigerated conditions by the near-infrared (NIR) spectroscopy and multivariate models. The NIR spectroscopy of diffuse transmission and reflection modes was used to compare the [...] Read more.
This work presents a novel work for the detection of the freshness of eggs stored at room temperature and refrigerated conditions by the near-infrared (NIR) spectroscopy and multivariate models. The NIR spectroscopy of diffuse transmission and reflection modes was used to compare the quantitative and qualitative investigation of egg freshness. It was found that diffuse transmission is more conducive to the judgment of egg freshness. The linear discriminant analysis model (LDA) for pattern recognition based on the diffuse transmission measurement was employed to analyze egg freshness during storage. NIR diffuse transmission spectroscopy showed great potential for egg storage time discrimination in normal atmospheric conditions. The LDA model discrimination rated up to 91.4% in the prediction set, while only 25.6% of samples were correctly discriminated among eggs in refrigerated storage conditions. Furthermore, NIR spectra, combined with the synergy interval partial least squares (Si-PLS) model, showed excellent ability in egg physical index prediction under normal atmospheric conditions. The root means square error of prediction (RMSEP) values of Haugh unit, yolk index, and weight loss from predictive Si-PLS models were 4.25, 0.031, and 0.005432, respectively. Full article
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
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14 pages, 13380 KiB  
Article
Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging
by Yuzhen Lu and Renfu Lu
Foods 2021, 10(5), 1094; https://doi.org/10.3390/foods10051094 - 14 May 2021
Cited by 8 | Viewed by 3394
Abstract
Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll [...] Read more.
Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers. Full article
(This article belongs to the Special Issue Nondestructive Optical Sensing for Food Quality and Safety Inspection)
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