Acoustic vibration technology: Toward a promising fruit quality detection method

Abstract

Understanding fruit quality is an effective way to reduce fruit loss and waste by providing proper strategies for fruit processing and management. Various noninvasive technologies have been developed for assessing fruit quality. Among them, acoustic vibration technology has received considerable attention from academics. As the fruit ripens, the physical and biochemical diversities of fruit cell lead to changes in the vibration characteristics of the fruit, which verified the feasibility of assessing fruit firmness by acoustic vibration technology from the microscopic view. Besides, the acoustic and vibration theories also provided the theoretical basis for this technology. The measurement system was mainly consisted of excitation devices, detection sensors, and signal processing modules. By using different excitation methods and devices, the fruit could demonstrate a free or forced vibration. The response signals of fruit were influenced by detection methods and sensors. To meet the requirement of high‐throughput detection, noncontact excitation devices and detection sensors were more suitable for on‐line applications. In addition, the relative locations of excitation and detection points and the posture style of fruit also had an impact on the measurement results, which should be determined before the test. Moreover, the proper data analysis method was equally important to extract potential parameters to improve the performance of prediction models. This paper provided a comprehensive overview concerning the principle, system composition, data analysis, and on‐line applications of acoustic vibration technology, and discussed the perspectives of future trends to promote the intensive study and further extension of this technology.

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

“…e authors of [17] combined the Gabor filter method with wavelet transform to identify the grayscale images of wood defects and introduced the multichannel Gabor filter, which could identify wood defects under the interference of complex background. e authors of [18] used principal component analysis to reduce the dimension of extracted features which can effectively identify and locate defects of complex inner holes. e authors of [19] input five features including the first three features of local binary mode and Tamura texture together with the entropy feature of gray co-occurrence matrix into the classifier of the support vector machine, and the recognition accuracy is 91.67%, which is better than that of BP neural network classifier, and the recognition accuracy of BP neural network is 82.75%.…”

Diseño y prueba de un equipo de espectroscopia de resonancia acústica en líquidos y sus potenciales usos

“…Se tomó en cuenta la posición y calibración del sensor y la fuente sonora, como el micrófono de tipo condensador y un audífono pequeño de tipo electrodinámico, ya que estos siempre deben estar a un mismo nivel y frente a frente. Además, se tomó en cuenta una estructura fija que sirviera como guía al vaso porta muestra la cual se hizo con teflón, ya que es importante que siempre quede en una misma posición [30], [31].…”

Tổng Quan Về Đánh Giá Chất Lượng Trái Cây Bằng Phương Pháp Không Phá Hủy

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