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Codling moth (CM) is one of the most devastating insect pests of apples in North America. Effective and early detection of external and internal CM infestation could remarkably reduce postharvest losses and improve the quality of local and exported apples. Hyperspectral imaging (HSI) has been used as a powerful tool for nondestructive defect detection and classification in agricultural products, with the advantage of providing both spectral and spatial features, and the ability to detect internal defects. These merits make HSI a suitable candidate for detecting CM infestation in apples, where the damage is mostly internal, occasionally with some hard-to-visualize surface symptoms such as holes and frass. In this study, a spectral-spatial classification method was used to distinguish CM-infested from non-infested apples based on near-infrared hyperspectral imaging (NIR HIS) in the wavelength range from 900 to 1700 nm with a 3.35 nm increment. Two approaches were applied and compared in this study. In the first approach mean reflectance spectra (MRS) were calculated for the image of the entire apple and the classification was performed with an overall test set classification rate of 81.04%. In the second approach, the fruit pixels were classified into two classes, control and infested, with a 99.24% total accuracy for the test data set from random forest (RF) classifier among others. These results indicate the high potential of NIR HSI in detecting and classifying CM infestation in apples.

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.

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.

Codling moth (CM) is the most devastating global pest of apples with a huge potential impact on the post-harvest quality and yield of the product. Due to the small size of its larvae and potentially hidden behavior, simple visual inspection is ill-suited for accurate infestation detection. The characteristic vibro-acoustic signals of multiple behaviors of CM larvae such as chewing and boring were identified in a previous study. In this study, two different approaches were proposed to build on this previous work: multi-domain feature extraction with machine learning to show basic classification potential, and matched filter-aided classification to show the effects of preprocessing using the larval behavior templates. Additionally, low-intensity heat stimulation was applied to improve classification results by increasing the larvae’s hidden activity rate. The results indicated that the first approach led to accuracies as high as 97.47 % for an acoustic signal duration of 10 s, with heat stimulation improving classification rates to 98.96 % for the same interval. Finally, the matched filter-aided classification approach improved upon the heat stimulated results even further to obtain a 100 % accuracy on classifying the test set for a signal duration of 5 s. These findings suggest that the vibro-acoustic technique can be an adaptable tool for detecting CM infestation in apples and improve post-harvest classification quality in fruit.

Effective detection of insect infestation is important for preserving quality of fresh fruits like apples. The objective of this research was to study the effectiveness of visible (VIS)/near-infrared (NIR) hyperspectral imaging (HSI) (400–900 nm) in the diffuse reflectance mode for detecting and classifying codling moth (CM) infestation in apples. A pushbroom HSI was implemented to acquire hyperspectral images for GoldRush apples of fresh-picked and stored at 4, 10, 17, and 27 °C, s for 4 months. Mean reflectance spectra (MRS) were calculated for the images and several classification techniques were applied including linear discriminant analysis (LDA), decision trees, K-nearest neighbor (Knn), partial least squares discriminant analysis (PLSDA), feed forward artificial neural networks (FFNN) in addition to majority voting. The most influential wavelengths were determined using the sequential forwards selection (SFS) approach. The highest classification performance was obtained using decision trees at 5 selected wavelengths (434.0 nm, 437.5 nm, 538.3 nm, 582.8 nm and 914.5 nm) with overall classification rate of 82% for the test set, and 81% and 86% for healthy and CM-infested apples. This study shows the potential of using VIS/NIR hyperspectral imaging as a non-destructive method for detecting CM infestation in apples.

There is a growing demand for developing effective non-destructive quality assessment methods with quick response, high accuracy, and low cost for fresh fruits. In this study, hyperspectral reflectance imaging (400 to 1000 nm) and acoustic emission (AE) tests were applied to ‘GoldRush’ apples (total number, n = 180) to predict fruit firmness, total soluble solids (TSS), and surface color parameters (L*, a*, b*) during an eight-week storage period. Partial least squares (PLS) regression, least squares support vector machine (LS-SVM), and multivariate linear regression (MLR) methods were used to establish models to predict the quality attributes of the apples. The results showed that hyperspectral imaging (HSI) could accurately predict all the attributes except TSS, while the AE method was capable of predicting fruit firmness, b* color index, and TSS. Overall, HSI regression using PLS had better comprehensive ability for predicting firmness, TSS, and color parameters (L*, a*, b*) than AE, with correlation coefficients of prediction (rp) of 0.92, 0.41, 0.83, 0.87, and 0.94 and root mean square errors of prediction (RMSEP) of 4.32 (N), 1.78 (°Brix), 3.41, 2.28, and 4.29, respectively, while AE regression using LS-SVM gave rp values of 0.88, 0.74, 0.34, 0.37, and 0.81 and RMSEP values of 4.26 (N), 0.64 (°Brix), 4.69, 1.8, and 5.17 for firmness, TSS, and color parameters (L*, a*, b*), respectively. The results show the potential of these two non-destructive methods for predicting some of the quality attributes of apples. Keywords: Apple, Acoustic emission, Fruit quality, Hyperspectral imaging, Regression model.

Mechanical property data of pomegranate fruits are limited. The objective of this study was to determine the compression and cutting properties of whole fruit as well as the puncture strength of peel samples of fruit at different storage times. The results showed that storage time had a significant effect on all the properties studied in this work except for peel shear strain (p < 0.05). The whole fruit cutting energy and power, maximum cutting force, peel shear strength and strain and shear modulus showed an increasing trend with storage time. However, firmness, bioyield force, and modulus of elasticity increased and then decreased with storage time.

An impingement method was employed for extracting of pomegranate arils. The method is based on opening the fruit, separating it into two halves, and extracting the arils with the help of pressurised air-jets. The results showed that air pressure, nozzle diameter, the number of passes, and the route of the nozzle all had significant effects ( P =0.01) on the percentage of extracted arils. Air pressure had the highest effect on aril extraction, while the nozzle diameter had the least. The best results of extraction of pomegranate arils were obtained when the nozzle travelled in a figure of eight route. Air pressure and nozzle diameter also significantly influenced the percentage of damaged pomegranate arils. Extracting the fruit arils at an air pressure of 800 kPa caused a considerable portion of extracted arils to be mechanically damaged. At an air pressure of 500 and 700 kPa the percentages of damaged arils were not appreciable. At 500 kPa air pressure, a nozzle diameter of 3.5 mm, and 4 passes of the nozzle the percentage of extracted arils was equal to 86%. The rupture force and energy of the aril flesh ranged between 7.32–8.35 N and 3.73–4.62 N mm, respectively. The mean values of measured impingement loads for air pressures of 300, 500, 700, and 800 kPa were found to be 2.49, 3.63, 5.00, and 8.32 N, respectively. Impingement force increased with nozzle diameter.