Literature DB >> 28613932

Nondestructive quality evaluation of agro-products using acoustic vibration methods-A review.

Wen Zhang1,2, Zhenzhen Lv3, Shuangli Xiong1,2.   

Abstract

Quality evaluation of agro-products is quite important because it is the basis for growers, distributers, and consumers. Various novel and emerging nondestructive methods were proposed for quality evaluation of agro-products. The acoustic vibration method is one of the major nondestructive methods for agro-products in pre- and postharvest research and industrial practice. Acoustic vibration characteristics of agro-products can be used for texture evaluation, prediction of optimum eating and harvest ripeness, ripeness classification and defect detection. Generally, there are three parts in the process of acoustic vibration method, including the excitation module, signal acquisition module, and signal-processing module. The impact method and forced method are two excitation methods in the excitation module, and there are contact and noncontact sensors for vibration measurement in the signal acquisition module. Noncontact measurement can meet the requirement of rapid and nondestructive measurement, especially for the on-line detection. However, increasing demand for accurate and cost-effective measurement remains a challenge in the agro-products industry. Comparison of acoustic vibration methods and traditional destructive methods was also discussed, which helps to give a more comprehensive assessment for the acoustic vibration method.

Keywords:  Nondestructive technique; acoustic vibration; agro-products; quality evaluation

Mesh:

Year:  2017        PMID: 28613932     DOI: 10.1080/10408398.2017.1324830

Source DB:  PubMed          Journal:  Crit Rev Food Sci Nutr        ISSN: 1040-8398            Impact factor:   11.176


  2 in total

1.  Nondestructive measurement of kiwifruit firmness, soluble solid content (SSC), titratable acidity (TA), and sensory quality by vibration spectrum.

Authors:  Wen Zhang; Aichen Wang; Zhenzhen Lv; Zongmei Gao
Journal:  Food Sci Nutr       Date:  2020-01-20       Impact factor: 2.863

2.  Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach.

Authors:  Dominique Albert-Weiss; Ahmad Osman
Journal:  Sensors (Basel)       Date:  2022-01-06       Impact factor: 3.576

  2 in total

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