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Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables

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A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Postharvest Biology, Quality, Safety, and Technology".

Deadline for manuscript submissions: closed (21 July 2023) | Viewed by 13193

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Dr. Ewa Ropelewska

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Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland
Interests: image analysis; machine vision; deep learning; neural network; hyperspectral imaging; multispectral imaging; computed tomography; horticulture; fruit quality; vegetable quality; fruit and vegetable processing
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Dear Colleagues,

The quality of fruit and vegetables may depend, among other things, on chemical and physical parameters, microbiological properties, sensory attributes, maturity, the presence of external and internal defects, and various types of damage. Fruit and vegetables of high quality are desired and more readily accepted by consumers, processors, and distributors. Nondestructive techniques for measuring the quality of fruit and vegetables provide objective and precise results without damaging the sample, which can then be used in further tests. Food quality evaluation using innovative nondestructive techniques may offer an alternative to commonly used traditional destructive methods and may have certain advantages over them. Nondestructive techniques usually allow for greater flexibility, throughput, and repeatability at a lower cost. Due to the ever-increasing importance of nondestructive techniques, they need to be constantly improved. Therefore, innovative solutions for improving measurements are desirable.

This Special Issue of Horticulturae will focus on innovations in the nondestructive testing of the quality of fruit and vegetables. Researchers involved in the improvement of nondestructive techniques for measuring the quality of fruit and vegetables as well as innovative methods for data processing are encouraged to submit their papers. Articles on, but not limited to, the following aspects would be appreciated: innovative imaging techniques for the evaluation of the external and internal structures of fruit and vegetables, including digital imaging, multispectral and hyperspectral imaging, fluorescence imaging, Raman imaging, laser-induced light backscattering imaging, thermal imaging, microwave imaging, magnetic resonance imaging, X-ray computed tomography; advances in spectroscopic techniques, ultrasonic techniques, electronic nose, and electronic tongue; and machine learning for data processing.

Prof. Dr. Ewa Ropelewska
Guest Editor

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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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

fruit and vegetable quality
innovative nondestructive techniques
imaging
spectroscopic techniques
ultrasonic techniques
electronic nose and electronic tongue
data processing
machine learning algorithms

Published Papers (7 papers)

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Research

10 pages, 2368 KiB

Open AccessArticle

Detection of Internal Browning Disorder in ‘Greensis’ Pears Using a Portable Non-Destructive Instrument

by Ho-Jin Seo and Janghoon Song

Horticulturae 2023, 9(8), 944; https://doi.org/10.3390/horticulturae9080944 - 19 Aug 2023

Cited by 2 | Viewed by 1219

Abstract

Internal browning caused by prolonged cold storage poses a significant challenge to the visual appearance and flavor of Asian pears, which are economically valuable and a primary fruit exported from Korea. To address this issue, we established a cost-effective portable non-destructive piece of testing instrument using visible and near-infrared spectroscopy, focusing on the detection and discrimination of internal browning in ‘Greensis’ pears. Our investigation underscores the challenge of visually confirming browning, necessitating alternative methods for accurate assessment. Through comprehensive analysis involving three to four segments of 32 ‘Greensis’ pears, a robust calibration equation was derived. By employing partial least square regression on the absorption spectra within a 650–950 nm range, we developed a predictive model for detecting and quantifying browning. Through principal component analysis, normal pears were distinctly segregated from those exhibiting browning symptoms (discrimination accuracy of 95%). Furthermore, we established that pears with a browning index of 25 ± 2.0 are highly susceptible to browning following extended cold storage. Consequently, our proposed portable non-destructive instrument serves as a pivotal tool for farmers and fruit distributors, enabling efficient and precise selection of high-quality pears in an instance. Overall, our study introduces a practical solution to a pressing issue in the Asian pear industry. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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13 pages, 1199 KiB

Open AccessArticle

Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches

by Deyla Prajna, María Álvarez, Marta Barea-Sepúlveda, José Luis P. Calle, Diding Suhandy, Widiastuti Setyaningsih and Miguel Palma

Horticulturae 2023, 9(7), 778; https://doi.org/10.3390/horticulturae9070778 - 7 Jul 2023

Cited by 1 | Viewed by 1128

Abstract

Civet coffee is the world’s most expensive and rarest coffee bean. Indonesia was the first country to be identified as the origin of civet coffee. First, it is produced spontaneously by collecting civet feces from coffee plantations near the forest. Due to limited stock, farmers began cultivating civets to obtain safe supplies of civet coffee. Based on this, civet coffee can be divided into two types: wild and fed. A combination of spectroscopy and chemometrics can be used to evaluate authenticity with high speed and precision. In this study, seven samples from different regions were analyzed using NIR Spectroscopy with various preparations: unroasted, roasted, unground, and ground. The spectroscopic data were combined with unsupervised exploratory methods (hierarchical cluster analysis (HCA) and principal component analysis (PCA)) and supervised classification methods (support vector machine (SVM) and random forest (RF)). The HCA results showed a trend between roasted and unroasted beans; meanwhile, the PCA showed a trend based on coffee bean regions. Combining the SVM with leave-one-out-cross-validation (LOOCV) successfully differentiated 57.14% in all sample groups (unground, ground, unroasted, unroasted–unground, and roasted–unground), 78.57% in roasted, 92.86% in roasted–ground, and 100% in unroasted–ground. However, using the Boruta filter, the accuracy increased to 89.29% for all samples, to 85.71% for unground and unroasted–unground, and 100% for roasted, unroasted–ground, and roasted–ground. Ultimately, RF successfully differentiated 100% of all grouped samples. In general, roasting and grinding the samples before analysis improved the accuracy of differentiating between wild and feeding civet coffee using NIR Spectroscopy. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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16 pages, 5758 KiB

Open AccessArticle

Quantitatively Determine the Iron Content in the Fruit of ‘Huangguan’ Pear Using Near-Infrared Spectroscopy

by Liangjun Li, Chen Li, Jing Fang, Xiaolong Chen, Wen Qin, Hanhan Zhang, Jing Xu, Bing Jia, Wei Heng, Xiu Jin and Li Liu

Horticulturae 2023, 9(7), 773; https://doi.org/10.3390/horticulturae9070773 - 6 Jul 2023

Viewed by 1232

Abstract

‘Huangguan’ pear has excellent quality, strong adaptability, and good socioeconomic value. Iron is one of the important trace elements in plants, and iron imbalance seriously affects the growth and development of pear trees and reduces their economic benefits. If the iron content in pear fruit can be easily and non-destructively detected using modern technology during the critical period of fruit development, it will undoubtedly help guide actual production. In this study, ‘Huangguan’ pear fruit was used as the research object, and the possibility of using the more convenient near-infrared spectroscopy (900~1700 nm) technology for nondestructive detection of the iron content in the peel and pulp of ‘Huangguan’ pear was explored. First, 12 algorithms were used to preprocess the original spectral data, and based on the original and the preprocessed spectral data, partial least squares regression and gradient boosting regression tree algorithms were used. A full-band prediction model of the iron content in the peel and pulp of ‘Huangguan’ pear was established, and the genetic algorithm was used to extract characteristic wavelengths, establish a characteristic wavelength prediction model, and evaluate the prediction effect of each model according to the coefficient of determination R² and the relative analysis error RPD. After comparison, we found that the prediction model with the best prediction of the iron content in the peel and pulp of ‘Huangguan’ pear reaches class A, and the prediction effect is good and meets expectations. This experiment shows that the use of near-infrared spectroscopy can achieve better prediction of the iron content in the peel and pulp of ‘Huangguan’ pear. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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20 pages, 3068 KiB

Open AccessArticle

Prediction of Soluble Solids Content by Means of NIR Spectroscopy and Relation with Botrytis cinerea Tolerance in Strawberry Cultivars

by Manuela Mancini, Luca Mazzoni, Rohullah Qaderi, Elena Leoni, Virginia Tonanni, Francesco Gagliardi, Franco Capocasa, Giuseppe Toscano and Bruno Mezzetti

Horticulturae 2023, 9(1), 91; https://doi.org/10.3390/horticulturae9010091 - 11 Jan 2023

Cited by 4 | Viewed by 1969

Abstract

Strawberry fruits are particularly appreciated by consumers for their sweet taste related to their soluble solids content (SSC). However, strawberries are characterized by a short shelf-life and high susceptibility to tissue infection, mainly by Botrytis cinerea. The SSC determination of strawberry fruit through traditional destructive techniques has some limitations related to the applicability, timing, and number of samples. The aims of this study are (i) to verify if any relation between SSC and B. cinerea susceptibility in the fruits of five strawberry cultivars occurs and (ii) to determine the SSC of strawberry fruits through near infrared spectroscopy (NIR). Principal component analysis was used to search for spectral differences among the strawberry genotypes. The partial least squares regression technique was computed in order to predict the SSC of the fruits collected during two harvesting seasons. Moreover, variable selection methods were tested in order to improve the models and get better predictions. The results demonstrated that there was a high correlation between SSC and B. cinerea susceptibility (R² up to 0.87). The SSC was predicted with a standard error of 0.84 °Brix and R²_p 0.75 (for the best model), which indicated the possibility to use the models for screening applications. NIR spectroscopy represents an important non-destructive alternative and finds remarkable applications in the agro-food market. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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12 pages, 1373 KiB

Open AccessArticle

Maturity, Ripening and Quality of ‘Donghong’ Kiwifruit Evaluated by the Kiwi-Meter™

by Wenjun Huang, Zhouqian Wang, Qi Zhang, Shaoran Feng, Jeremy Burdon and Caihong Zhong

Horticulturae 2022, 8(9), 852; https://doi.org/10.3390/horticulturae8090852 - 18 Sep 2022

Cited by 2 | Viewed by 2286

Abstract

Traditional destructive fruit assessment methodologies are currently being replaced by non-destructive alternatives. The Kiwi-Meter™ is promoted as a non-destructive device for assessment of kiwifruit maturation and ripening. In this study, three trials evaluated the feasibility of using the Kiwi-Meter and its I_AD™ index data for monitoring maturation, ripening, and quality of Actinidia chinensis var. chinensis ‘Donghong’ kiwifruit. The findings from the trials suggest that the Kiwi-Meter provides a non-destructive tool for measuring the color or chlorophyll content of the outer tissues of ‘Donghong’ kiwifruit. Since the timing of harvest of kiwifruit is not determined solely by flesh color, the utility of the Kiwi-Meter in any wider evaluation of fruit maturation (or ripening or quality) is dependent on there being a strong association between other fruit characteristics of interest with flesh color. The ‘Donghong’ fruit used in this trial degreened fully before ripening and thus the Kiwi-Meter could not provide a measure of maturation, ripening, or fruit quality. It is concluded that the Kiwi-Meter can assess fruit for flesh color, although even for this purpose, it must be considered that the I_AD measurement may be limited to only the outer area of the fruit flesh. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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16 pages, 4864 KiB

Open AccessEditor’s ChoiceArticle

Multi-Band-Image Based Detection of Apple Surface Defect Using Machine Vision and Deep Learning

by Yan Tang, Hongyi Bai, Laijun Sun, Yu Wang, Jingli Hou, Yonglong Huo and Rui Min

Horticulturae 2022, 8(7), 666; https://doi.org/10.3390/horticulturae8070666 - 21 Jul 2022

Cited by 7 | Viewed by 2155

Abstract

Accurate surface defect extraction of apples is critical for their quality inspection and marketing purposes. Using multi-band images, this study proposes a detection method for apple surface defects with a combination of machine vision and deep learning. Five single bands, 460, 522, 660, 762, and 842 nm, were selected within the visible and near-infrared. By using a near-infrared industrial camera with optical filters, five single-band images of an apple could be obtained. To achieve higher accuracy of defect extraction, an improved U-Net was designed based on the original U-Net network structure. More specially, the partial original convolutions were replaced by dilated convolutions with different dilated rates, and an attention mechanism was added. The loss function was also redesigned during the training process. Then the traditional algorithm, the trained U-Net and the trained improved U-Net were used to extract defects of apples in the test set. Following that, the performances of the three methods were compared with that of the manual extraction. The results show that the near-infrared band is better than the visible band for defects with insignificant features. Additionally, the improved U-Net is better than the U-Net and the traditional algorithm for small defects and defects with irregular edges. On the test set, for single-band images at 762 nm, the improved U-Net had the best defect extraction with an mIoU (mean intersection over union) and mF1-score of 91% and 95%, respectively. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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9 pages, 1252 KiB

Open AccessFeature PaperArticle

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

by Ewa Ropelewska, Kadir Sabanci, Muhammet Fatih Aslan and Afshin Azizi

Horticulturae 2022, 8(5), 431; https://doi.org/10.3390/horticulturae8050431 - 11 May 2022

Cited by 5 | Viewed by 1832

Abstract

The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis. Full article

(This article belongs to the Special Issue Innovative Nondestructive Techniques to Improve Quality Measurement of Fruits and Vegetables)

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