A review of non-destructive methods for quality evaluation and sorting of agricultural products

AbstractThis work studies the evolution in time of several varieties of apples with application in quality storage maintenance. Two different methods were used to evaluate long-stored apples for better sorting and degradation assessment. The first method was laser photoacoustic spectroscopy for the detection of ethylene and ethanol compounds from the internal atmosphere of apples. The second method was multispectral imaging that measures the image and the spectrum combined and also can be used to address features such as ripening and external defects. The experiments showed that, the ethylene value decreases and the value of ethanol increases, which sometimes we may associate with a drift of the images toward darker tones, because the apple is slowly degrading. Non-invasive, real-time inspection can reveal when the degradation process begins, improving the capability of sorting, maintaining their quality and storability.

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Potential application of laser-based imaging technology in the quality evaluation of agricultural products : A review

Advances in Agricultural and Food Research Journal ◽

10.36877/aafrj.a0000127 ◽

2020 ◽

Vol 1 (2) ◽

Author(s):

Philip Donald Cabuga Sanchez ◽

NORHASHILA HASHIM ◽

ROSNAH SHAMSUDIN ◽

MOHD ZUHAIR MOHD NOR

Keyword(s):

Potential Application ◽

Quality Evaluation ◽

Imaging Technique ◽

Current Knowledge ◽

Agricultural Products ◽

Primary Concern ◽

Evaluation Tool ◽

Imaging Technology ◽

Non Invasive ◽

Non Destructive

Non-destructive quality evaluation of agricultural products particularly during postharvest stage has been a primary concern in recent years. The laser-based imaging technology is one of the most promising non-invasive tools which demonstrate potential ability to replace the conventional methods of quality monitoring that are time-consuming, expensive, laborious, inaccurate and most of all destructive. Hence, in this paper, we briefly reviewed the potential application of laser-light backscattering imaging technique (LLBI) as a non-destructive quality evaluation tool applied in agricultural products. This review mainly reports the current knowledge on the successful implementations of the LLBI in measuring the various quality-related attributes of agricultural products under different postharvest conditions such as in drying, storage, sorting, maturity identification, defect detection, etc. The basic components, uses and considerations of the technique are highlighted in this paper. Moreover, the advantages, drawbacks, measurement methods, data analysis applied as well as the accuracies obtained are briefly summarized.

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A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array

Sensors ◽

10.3390/s21175836 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5836

Author(s):

Ali Khorramifar ◽

Mansour Rasekh ◽

Hamed Karami ◽

Urszula Malaga-Toboła ◽

Marek Gancarz

Keyword(s):

Food Industry ◽

Sensor Array ◽

Potato Cultivars ◽

Agricultural Products ◽

Advanced Technologies ◽

Best Response ◽

Main Components ◽

Non Destructive ◽

Different Gases ◽

Destructive Methods

In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples’ dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.

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