Evaluation of apple texture with contact acoustic emission detector: A study on performance of calibration models

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.

Fusion of acoustic sensing and deep learning techniques for apple mealiness detection

Mealiness in apple fruit can occur during storage or because of harvesting in an inappropriate time; it degrades the quality of the fruit and has a considerable role in the fruit industry. In this paper, a novel non-destructive approach for detection of mealiness in Red Delicious apple using acoustic and deep learning techniques was proposed. A confined compression test was performed to assign labels of mealy and non-mealy to the apple samples. The criteria for the assignment were hardness and juiciness of the samples. For the acoustic measurements, a plastic ball pendulum was used as the impact device, and a microphone was installed near the sample to record the impact response. The recorded acoustic signals were converted to images. Two famous pre-trained convolutional neural networks, AlexNet and VGGNet were fine-tuned and employed as classifiers. According to the result obtained, the accuracy of AlexNet and VGGNet for classifying the apples to the two categories of mealy and non-mealy apples was 91.11% and 86.94%, respectively. In addition, the training and classification speed of AlexNet was higher. The results indicated that the suggested method provides an effective and promising tool for assessment of mealiness in apple fruit non-destructively and inexpensively.

Analysis of pectin biopolymer phase states using acoustic emissions

Acoustic emissions are stress or elastic waves produced by a material under external load. Since acoustic emissions are generated from within and transmitted through the substance, the acoustic signature provides insights into the physical and mechanical properties of the material. In this report, we used a constant velocity probe with force and acoustic emission monitoring to investigate the properties of glass phase and gel phase pectin films. In the gel phase films, a constant velocity uniaxial load produced periodic premonitory acoustic emissions with coincident force variations (saw-tooth pattern). SEM images of the gel phase microarchitecture indicated the presence of slip planes. In contrast, the glass phase films demonstrated early acoustic emissions, but effectively no force or acoustic evidence of periodic or premonitory emissions. Microstructural imaging of the glass phase films indicated the presence of early microcracks as well as dense polymerization of the pectin (without evidence of slip planes). We conclude that the water content in the pectin films contributes to not only the physical properties of the films, but also the stick-slip motion observed with constant uniaxial load. Further, acoustic emissions provide a sensitive and practical measure of this mechanical behavior.

Applications of Non-destructive Technologies for Agricultural and Food Products Quality Inspection

The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation.