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Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (10 March 2022) | Viewed by 36815

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Special Issue Editor

Dr. Vassilis S. Kodogiannis

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Guest Editor

School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW, UK
Interests: neural networks; fuzzy systems; genetic algorithms; hybrid systems; machine learning; image/signal processing; bio-signal analysis; chemometrics; control; non-invasive sensing systems; robotics
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Special Issue Information

Dear Colleagues,

Food is routinely screened to assess quality (such as physical appearance and organoleptic properties) and safety (absence of health threatening pathogens and chemical compounds). These tests are usually carried out in laboratory by skilled personnel, thus resulting in delayed response and high costs for the analysis. No longer restricted to detailed laboratory analyses or simplified implementation in industrial or commercial settings, non-invasive sensing methods can now accommodate non-destructive, comprehensive, high-resolution spectral and image analyses for real-world safety and quality inspection on rapid food-processing lines.

In this context, analytical techniques, such as spectroscopy (UV-Vis, NIR, Raman, NMR, fluorescence, ultrasound, etc.), electronic nose, electronic tongue, nano-sensors and imaging (digital, hyperspectral, multispectral) play a key role. These techniques offer the possibility of simultaneously determining a high number of compounds or features, the so-called “fingerprint,” analyzing samples in a non-destructive, easy, quick, and direct way with minimal sample preparation. The resulting datasets are usually high dimensional and complex, requiring methods of pattern recognition or predictive analysis to extract important information. This special issue welcomes applications, high-quality articles on the application of non-invasive methods and machine learning based techniques to analyse or monitor composition, adulteration, quality and authentication issues in a diverse range of food (such as meats, fish, fruits, vegetables, oil, wines and dairy) products.

Dr. Vassilis S. Kodogiannis
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

Topics of this special issue will include, but are not limited to:

Spectroscopic techniques (UV-VIS, NIR, Raman, NMR, fluorescence, etc)
Electronic nose and tongue
Imaging methods (digital, hyperspectral, etc.)
Fusion of multiple sensors applied to Food Analysis
Machine learning techniques for Food Quality Inspection
Deep learning for Automated Food Inspection
Detection of food adulteration using Deep and Ensemble Learning
Feature selection and extraction methods to improve classiffication tasks
Food authentication, adulteration
Food Quality evalution (incl. spoilage, freshness)
Food composition

Published Papers (11 papers)

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Research

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16 pages, 3002 KiB

Open AccessArticle

Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

by Yang Cao, Yuchen Zhang, Menghua Lin, Di Wu and Kunsong Chen

Sensors 2022, 22(2), 427; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020427 - 06 Jan 2022

Cited by 9 | Viewed by 2124

Abstract

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography–mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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14 pages, 12763 KiB

Open AccessArticle

Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

by Dong-Hoon Kwak, Guk-Jin Son, Mi-Kyung Park and Young-Duk Kim

Sensors 2021, 21(16), 5279; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165279 - 04 Aug 2021

Cited by 9 | Viewed by 2576

Abstract

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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16 pages, 5652 KiB

Open AccessArticle

Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks

by Marcin Malesa and Piotr Rajkiewicz

Sensors 2021, 21(2), 501; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020501 - 12 Jan 2021

Cited by 16 | Viewed by 3556

Abstract

Product quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visual issues, additional safety regulations are demanded. Many production processes can be controlled completely contactless through the use of machine vision cameras and advanced image processing techniques. The most dynamically growing sector of image analysis methods are solutions based on deep neural networks. Their major advantages are fast performance, robustness, and the fact that they can be exploited even in complicated classification problems. However, the use of machine learning methods on high-performance production lines may be limited by inference time or, in the case of multiformated production lines, training time. The article presents a novel data preprocessing (or calibration) method. It uses prior knowledge about the optical system, which enables the use of the lightweight Convolutional Neural Network (CNN) model for product quality control of polyethylene terephthalate (PET) bottle caps. The combination of preprocessing with the lightweight CNN model resulted in at least a five-fold reduction in prediction and training time compared to the lighter standard models tested on ImageNet, without loss of accuracy. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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12 pages, 2506 KiB

Open AccessArticle

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

by Baohua Yang, Yuan Gao, Qian Yan, Lin Qi, Yue Zhu and Bing Wang

Sensors 2020, 20(18), 5021; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185021 - 04 Sep 2020

Cited by 22 | Viewed by 2620

Abstract

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R² = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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13 pages, 14664 KiB

Open AccessArticle

Non-Destructive Detection of Bone Fragments Embedded in Meat Using Hyperspectral Reflectance Imaging Technique

by Jongguk Lim, Ahyeong Lee, Jungsook Kang, Youngwook Seo, Balgeum Kim, Giyoung Kim and Seong Min Kim

Sensors 2020, 20(14), 4038; https://0-doi-org.brum.beds.ac.uk/10.3390/s20144038 - 21 Jul 2020

Cited by 10 | Viewed by 4304

Abstract

Meat consumption has shifted from a quantitative to a qualitative growth stage due to improved living standards and economic development. Recently, consumers have paid attention to quality and safety in their decision to purchase meat. However, foreign substances which are not normal food ingredients are unintentionally incorporated into meat. These should be eliminated as a hazard to quality or safety. It is important to find a fast, non-destructive, and accurate detection technique of foreign substance in the meat processing industry. Hyperspectral imaging technology has been regarded as a novel technology capable of providing large-scale imaging and continuous observation information on agricultural products and food. In this study, we considered the feasibility of the short-wave near infrared (SWIR) hyperspectral reflectance imaging technique to detect bone fragments embedded in chicken meat. De-boned chicken breast samples with thicknesses of 3, 6, and 9-mm and 5 bone fragments with lengths of about 20–30-mm are used for this experiment. The reflectance spectra (in the wavelength range from 987 to 1701-nm) of the 5 bone fragments embedded under the chicken breast fillet are collected. Our results suggested that these hyperspectral imaging technique is able to detect bone fragments in chicken breast, particularly with the use of a subtraction image (corresponding to image at 1153.8-nm and 1480.2-nm). Thus, the SWIR hyperspectral reflectance imaging technique can be potentially used to detect foreign substance embedded in meat. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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16 pages, 1927 KiB

Open AccessArticle

Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network

by Belén Curto, Vidal Moreno, Juan Alberto García-Esteban, Francisco Javier Blanco, Inmaculada González, Ana Vivar and Isabel Revilla

Sensors 2020, 20(12), 3566; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123566 - 24 Jun 2020

Cited by 17 | Viewed by 3452

Abstract

The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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14 pages, 3870 KiB

Open AccessArticle

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods

by Shizhuang Weng, Shuan Yu, Binqing Guo, Peipei Tang and Dong Liang

Sensors 2020, 20(11), 3074; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113074 - 29 May 2020

Cited by 38 | Viewed by 3460

Abstract

Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400–1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (R_p²) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with R_p² = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with R_p² = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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14 pages, 6212 KiB

Open AccessArticle

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

by Zhiming Guo, Chuang Guo, Quansheng Chen, Qin Ouyang, Jiyong Shi, Hesham R. El-Seedi and Xiaobo Zou

Sensors 2020, 20(7), 2130; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072130 - 09 Apr 2020

Cited by 25 | Viewed by 3472

Abstract

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: R_c = 0.953, root mean square error of calibration (RMSEC) = 1.28, R_p = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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18 pages, 3272 KiB

Open AccessArticle

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

by Baohua Yang, Lin Qi, Mengxuan Wang, Saddam Hussain, Huabin Wang, Bing Wang and Jingming Ning

Sensors 2020, 20(1), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010050 - 20 Dec 2019

Cited by 22 | Viewed by 3598

Abstract

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R² = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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14 pages, 2333 KiB

Open AccessLetter

Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging

by Asma Khan, Muhammad Tajammal Munir, Wei Yu and Brent Young

Sensors 2020, 20(16), 4645; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164645 - 18 Aug 2020

Cited by 12 | Viewed by 2746

Abstract

Hyperspectral imaging (HSI) in the spectral range of 400–1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC₁ and PC₂) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (R_p²) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had R_p² of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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8 pages, 1394 KiB

Open AccessTechnical Note

Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees

by Zhong Zheng, Xin Zhang, Jinxing Yu, Rui Guo and Lili Zhangzhong

Sensors 2020, 20(4), 1223; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041223 - 23 Feb 2020

Cited by 9 | Viewed by 3089

Abstract

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)—the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)—are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future. Full article

(This article belongs to the Special Issue Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection)

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