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Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 33242

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Special Issue Editors

Dr. Akinbode A. Adedeji

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Guest Editor

Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA
Interests: hyperspectral imaging; nondestructive method; extrusion; plant-based protein
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Michael Ngadi

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Guest Editor

Department of Bioresource Engineering, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
Interests: food processing; food science and technology; food processing and engineering; food preservation; food safety; food engineering; food quality; food technology; processing technology; food texture; processing; food rheology; post harvest technology; dehydration; postharvest handling; food and nutrition
Special Issues, Collections and Topics in MDPI journals

Dr. Laura Liu

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Guest Editor

Food and Bioprocess Research Group, Department of Bioresource Engineering, McGill University, Montreal, Canada
Interests: hyperspectral imaging; computer vision; machine learning; artificial intelligence; food quality and safety

Special Issue Information

Dear Colleagues,

The recent pandemic reinforces the need for more nondestructive technology development within the agricultural and food processing sectors in order to prevent the disruption experienced in the food supply chain at the peak of the spread of the virus. There are several other advantages to nondestructive detection and characterization of biological materials such as foods. The product is preserved and reusable after testing, often rapid, and its application is easy; additionally, it removes the drudgery from the process, and it is a ready tool in robotics application in food. Nondestructive methods have the potential to increase sustainability within the agricultural production and food processing industry, through reduced waste, increased safety of our foods, efficient production and processing, and reduced cost. With recent advances in computational analysis, especially with improved machine learning methods and deep learning methods, the accuracy of prediction of food properties is improving, and this is pushing the boundaries of applications with increased reliability. This Special Issue will disseminate recent advances in innovation, technology, and application of the nondestructive approach for food and agricultural product quality determination.

Dr. Akinbode A. Adedeji
Prof. Dr. Michael Ngadi
Dr. Laura Liu
Guest Editors

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Keywords

novel nondestructive methods for food quality determination
advances in traditional methods for nondestructive testing of foods: Raman spectroscopy, optical sensing, electronic nose, near-infrared spectroscopy, computer vision
hyperspectral imaging application in biological material quality determination
vibroacoustic emission method application in food quality detection
machine and deep learning application in food quality determination
model and sensor data fusion approach for improved data analytics
artificial intelligence and robotics for improved quality characterization

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Published Papers (7 papers)

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Research

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24 pages, 5100 KiB

Open AccessArticle

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

by Jayanta Kumar Basak, Bolappa Gamage Kaushalya Madhavi, Bhola Paudel, Na Eun Kim and Hyeon Tae Kim

Foods 2022, 11(14), 2086; https://doi.org/10.3390/foods11142086 - 13 Jul 2022

Cited by 42 | Viewed by 8777

Abstract

Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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11 pages, 421 KiB

Open AccessArticle

Quality Evaluation and Mathematical Modelling Approach to Estimate the Growth Parameters of Total Viable Count in Sausages with Different Casings

by Chao-Hui Feng

Foods 2022, 11(5), 634; https://doi.org/10.3390/foods11050634 - 22 Feb 2022

Cited by 11 | Viewed by 2817

Abstract

The growth kinetics for the total viable count (TVC) in sausages with modified hog casings (treated by surfactant solutions and slush salt with lactic acid), natural hog casings and sheep casings as a function of the storage time (up to 50 days) were studied for the first time. The growth of TVC was fitted by the Baranyi model, and the maximum specific growth rate, lag time and initial and final cell populations were estimated via DMFit. The coefficient of determination of the Baranyi model reached 0.94, 0.77 and 0.86 for sausages stuffed in modified hog casings (MHC), control hog casings (CHC) and natural sheep casings (NSC), respectively. The experimental data for the initial populations were 4.69 ± 0.10 log cfu/g for MHC, 4.79 ± 0.10 log cfu/g for CHC and 3.74 ± 0.14 log cfu/g for NSC, whilst the predicted initial cell populations for MHC, CHC and NSC were 4.81 ± 0.20 log cfu/g, 5.19 ± 0.53 log cfu/g and 3.74 ± 0.54 log cfu/g, respectively. Their shelf lives can also be predicted. The results show that the average pH value of MHC samples (6.96 ± 0.01) was significantly lower than that of CHC (7.09 ± 0.01) and NSC (7.05 ± 0.02) samples at day 50 (p < 0.05). Sausages with CHC possessed a significant higher water holding capacity (99.48 ± 0.14%) at d 29 than those with MHC (97.40 ± 0.46%) and NSC (98.55 ± 0.17%) (p < 0.05). On the last day, the average moisture content for samples with NSC (38.30 ± 3.23%) was significantly higher than that for those with MHC (29.38 ± 2.52%) and CHC (29.15 ± 1.16%) (p < 0.05). Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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15 pages, 1867 KiB

Open AccessArticle

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

by Sonia Nieto-Ortega, Ángela Melado-Herreros, Giuseppe Foti, Idoia Olabarrieta, Graciela Ramilo-Fernández, Carmen Gonzalez Sotelo, Bárbara Teixeira, Amaya Velasco and Rogério Mendes

Foods 2022, 11(1), 55; https://doi.org/10.3390/foods11010055 - 27 Dec 2021

Cited by 12 | Viewed by 4513

Abstract

The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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16 pages, 1514 KiB

Open AccessArticle

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

by Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva and Akinbode A. Adedeji

Foods 2022, 11(1), 8; https://doi.org/10.3390/foods11010008 - 21 Dec 2021

Cited by 20 | Viewed by 4444

Abstract

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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13 pages, 3060 KiB

Open AccessArticle

Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions

by Ching-Wei Cheng, Kun-Ming Lai, Wan-Yu Liu, Cheng-Han Li, Yu-Hsun Chen and Chien-Chung Jeng

Foods 2021, 10(9), 2057; https://doi.org/10.3390/foods10092057 - 31 Aug 2021

Cited by 1 | Viewed by 2916

Abstract

Although many ultraviolet-visible-near-infrared transmission spectroscopy techniques have been applied to chicken egg studies, such techniques are not suitable for duck eggs because duck eggshells are much thicker than chicken eggshells. In this study, a high-transmission spectrometer using an equilateral prism as a dispersive element and a flash lamp as a light source was constructed to nondestructively detect the transmission spectrum of duck eggs and monitor the pickling of eggs. The evolution of egg transmittance was highly correlated with the albumen during pickling. The transmittance exponentially decays with time during this period, and the decay rate is related to the pickling rate. The colors of the albumen and yolk remain almost unchanged in the first stage. A multiple linear regression analysis model that realizes a one-to-one association between the days of pickling and the transmission spectra was constructed to determine the pickling duration in the second stage. The coefficient of determination reached 0.88 for a single variable, wavelength, at 590 nm. This method can monitor the maturity of pickled eggs in real time and does not require the evolution of light transmittance. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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14 pages, 24405 KiB

Open AccessArticle

Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations

by Xiaoguang Dong, Libing Gao, Haijun Zhang, Jing Wang, Kai Qiu, Guanghai Qi and Shugeng Wu

Foods 2021, 10(9), 1984; https://doi.org/10.3390/foods10091984 - 25 Aug 2021

Cited by 16 | Viewed by 4082

Abstract

The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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Review

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31 pages, 2727 KiB

Open AccessReview

Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review

by Emmanuel Ekene Okere, Ebrahiema Arendse, Helene Nieuwoudt, Olaniyi Amos Fawole, Willem Jacobus Perold and Umezuruike Linus Opara

Foods 2021, 10(12), 3061; https://doi.org/10.3390/foods10123061 - 9 Dec 2021

Cited by 17 | Viewed by 4263

Abstract

This review covers recent developments in the field of non-invasive techniques for the quality assessment of processed horticultural products over the past decade. The concept of quality and various quality characteristics related to evaluating processed horticultural products are detailed. A brief overview of non-invasive methods, including spectroscopic techniques, nuclear magnetic resonance, and hyperspectral imaging techniques, is presented. This review highlights their application to predict quality attributes of different processed horticultural products (e.g., powders, juices, and oils). A concise summary of their potential commercial application for quality assessment, control, and monitoring of processed agricultural products is provided. Finally, we discuss their limitations and highlight other emerging non-invasive techniques applicable for monitoring and evaluating the quality attributes of processed horticultural products. Our findings suggest that infrared spectroscopy (both near and mid) has been the preferred choice for the non-invasive assessment of processed horticultural products, such as juices, oils, and powders, and can be adapted for on-line quality control. Raman spectroscopy has shown potential in the analysis of powdered products. However, imaging techniques, such as hyperspectral imaging and X-ray computed tomography, require improvement on data acquisition, processing times, and reduction in the cost and size of the devices so that they can be adopted for on-line measurements at processing facilities. Overall, this review suggests that non-invasive techniques have the potential for industrial application and can be used for quality assessment. Full article

(This article belongs to the Special Issue Applications of Non-destructive Technologies for Agricultural and Food Product Quality Determination)

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