Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars

Early detection of slight bruises in apples by cost-efficient near-infrared imaging

Near-infrared (NIR) spectroscopy has been widely reported for its useful applications in assessing internal fruit qualities. Motivated by apple consumption in the global market, this study aims to evaluate the possibility of applying NIR imaging to detect slight bruises in apple fruits. A simple optical setup was designed, and low-cost system components were used to promote the future development of practical and cost-efficient devices. To evaluate the effectiveness of the proposed approach, slight bruises were created by a mild impact with a comparably low impact energy of only 0.081 Joules. Experimental results showed that 100% of bruises in Jazz and Gala apples were accurately detected immediately after bruising and within 3 hours of storage. Thus, it is promising to develop customer devices to detect slight bruises for not only apple fruits but also other fruits with soft and thin skin at their early damage stages

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Detection of Invisible Damages in `Rojo Brillante¿ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics

[EN] The main cause of flesh browning in 'Rojo Brillante' persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450-1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares-discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%.This work is co-funded by the projects AEI PID2019-107347RR-C31, PID2019-107347RR-C32, PID2019-107347RR-C33, IVIA-GVA 51918 and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014-2020.Munera, S.; Rodríguez-Ortega, A.; Aleixos Borrás, MN.; Cubero, S.; Gómez-Sanchis, J.; Blasco, J. (2021). Detection of Invisible Damages in `Rojo Brillante¿ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics. Foods. 10(9):1-12. https://doi.org/10.3390/foods1009217011210

Multispectral Method for Apple Defect Detection using Hyperspectral Imaging System

Hyperspectral imaging is a non-destructive detection technology and a powerful analytical tool that integrates conventional imaging and spectroscopy to get both spatial and spectral information from the objects for food safety and quality analysis. A recently developed hyperspectral imaging system was used to investigate the wavelength between 530nm and 835nm to detect defects on Red Delicious apples. The combination of band ratio method and relative intensity method were developed in this paper, which using the multispectral wavebands selected from hyperspectral images. The results showed that the hyperspectral imaging system with the properly developed multispectral method could generally identify 95% of the defects on apple surface accurately. The developed algorithms could help enhance food safety and protect public health while reducing human error and labor cost for food industr

Visible and Hyperspectral Imaging Systems for the Detection and Discrimination of Mechanical and Microbiological Damage of Mushrooms

Horticultural products such as mushrooms are exposed to environmental conditions during their postharvest life, which may affect product quality. Loss of whiteness during storage is particularly important in the mushroom industry. Rough handling and distribution, fruiting body senescence and bacterial infections are among the main causes of mushroom discolouration. The aim of this work was to study the use of visible and hyperspectral imaging (HSI) systems for the detection and discrimination of mechanical and microbiological damage of mushrooms. This piece of research involved a) monitoring the browning of mushroom with visible computer imaging systems, b) investigating the effect of mechanical damage on the kinetics of enzymes responsible for mushroom browning, c) exploring the potential use of Vis-NIR HSI to predict PPO activity in mushroom caps and d) studying the potential application of Vis-NIR HSI for microbial and viral detection on mushroom caps and for their discrimination from mechanical damage. Results presented in this thesis show that the efficacy of commercial webcams was limited in the detection of mechanical damage on mushroom caps. Damage increased the activity of PPOs on mushroom pileipellis, but the effect of the extent of damage was not significant at the levels of study. Vis-NIR HSI showed some potential as a tool to estimate the activity of PPO enzymes on mushroom caps. The combination of HSI with chemometric tools allowed for the differentiation of mechanically and microbiologically damaged mushroom classes. Results from this study could be used for developing non-destructive monitoring systems for mechanical and microbiological damage detection and discrimination. The potential application of such systems as on-line process analytical tools would facilitate rapid assessment of mushroom quality.

Prediction of Polyphenol Oxidase Activity Using Visible Near-Infrared Hyperspectral Imaging on Mushroom (Agaricus bisporus) Caps.

Physical stress (i.e. bruising) during harvesting, handling and transportation triggers enzymatic discoloration of mushrooms, a common and detrimental phenomenon largely mediated by polyphenol oxidase (PPO) enzymes. Hyperspectral imaging (HSI) is a non-destructive technique that combines imaging and spectroscopy to obtain information from a sample. The objective of this study was to assess the ability of HSI to predict the activity of PPO on mushroom caps. Hyperspectral images of mushrooms subjected to various damage treatments were taken, followed by enzyme extraction and PPO activity measurement. Principal component regression (PCR) models (each with 3 PCs) built on raw reflectance and multiple scatter corrected (MSC) reflectance data were found to be the best modeling approach. Prediction maps showed that the MSC model allowed for compensation of spectral differences due to sample curvature and surface irregularities. Results reveal the possibility of developing a sensor which could rapidly identify mushrooms with higher likelihood to develop enzymatic browning and hence aid produce management decision makers in the industry