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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.

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Abstract. Incidence of codling moth (CM) ( L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. ‘GoldRush’ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect the data emitted by 80 larvae-infested and intact apples in total. Eleven AE features that changed with signaling time were obtained with the AE system. For each feature, the area under the curve along the signaling time was calculated and used as an independent input variable for the machine learning algorithms, which included linear discriminant analysis (LDA) and ensemble method adaptive boosting. With signaling times ranging from 0.5 to 120 s, classification rates for infested versus intact apples ranged from 91% to 100% for the training set and from 83% to 100% for the test set. The quick signal collection and high classification accuracy obtained in this study show the potential of AE for detecting and classifying CM-infested apples. Keywords: Acoustic emission, Apple, Codling moth, Machine learning, Pest infestation.

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