Online Detection and Classification of Moldy Core Apples by Vis-NIR Transmittance Spectroscopy

Abstract

Apple moldy core is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using near-infrared transmittance spectroscopy in spectral range of 600–1100 nm. A total of 120 apple samples were selected and randomly divided into a training set and a test set based on the ratio of 2:1. First, basic parameters for detection of apples with moldy core were determined through detection experiments of samples in a stationary state. Due to the random distribution of the diseased tissue inside diseased apples, stationary detection cannot accurately identify the diseased tissue. To solve this problem, the spectra of apples in motion state transmitted forward by the transmission line were acquired. Three placement orientations of the apple in the carrying fruit cup were tested to explore the influence of fruit orientation on spectral characteristics and prediction. According to the performance of the model, the optimal preprocessing method and modeling method were determined (fixed orientation model and arbitrary orientation model). SPA was used to select the characteristic wavelengths to further improve the online detection speed. The overall results showed that the multi-spectra model using mean spectra of three orientations was the best. The prediction accuracies of multi-spectra model using SPA for three orientations for three orientations were 96.7%, 97.5% and 97.5% respectively. As a conclusion, the arbitrary orientation model was beneficial to improve the online detection of apple moldy core disease.

Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra

“…The spectra of the samples were acquired in two orientations, scanning upright pulps from 2018 and stable pulps from 2019, and the optimal model showed R 2 values of 0.88 for DMC calibration, 0.83 for DMC prediction, 0.70 for SSC calibration, and 0.70 for SSC prediction, with an RMSEP of 4.32% and 4.0%, respectively, and a RPIQ of 3.52 for DMC and 2.2 for SSC prediction [6]. Several other studies have explored the application of the inline measurement of NIR spectra for the analysis of different fruit, such as apple, orange, pear, strawberry, and mango, including the classification and prediction of specific constituents [13][14][15][16][17]. The results obtained from these studies provide clear evidence that the utilization of the inline measurement of NIR spectra is effective in achieving accurate classification and the precise prediction of specific constituents.…”

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

“…Near-infrared (NIR) spectroscopic imaging combines the advantages of spectroscopic and imaging techniques and is widely used as a mature non-destructive detection technique in the fields of agriculture and forestry, pharmaceuticals, food, petrochemicals, and tobacco [7][8][9][10][11]. There are information redundancy, noise, and background factors in the variables of spectral measurements.…”

Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies

“…Amuah et al [22] used a portable NIR spectrometer (740-1070 nm) to predict TSS with the results of R P = 0.854 and RMSEP = 0.842 • Brix by a PLSR model. In recent years, transmittance spectroscopy techniques have been increasingly studied for detection of the internal quality of fruits, including pomegranate [23], watermelon [24], pomelo [25], pear [26], apple [27,28], which showed the potential application prospect of such techniques coupled with appropriate modeling algorithms. Among these modeling algorithms, the PLS, ANN, and support vector machine (SVM) have been commonly used in spectral analyses for quantitative or qualitative analysis purposes.…”