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Potatoes are generally consumed directly as a staple food or used for processing, depending on the quality of raw materials. Dry matter content (DMC) is the most critical characteristic of potatoes, as it determines the processing and the final product quality. This study aimed to investigate the potential of different optical sensing systems in predicting the DMC of intact potato tubers, and the efficacy of classifying potatoes based on dry matter levels. The whole tubers were scanned using three optical sensing modes (transmittance spectra, interactance spectra and hyperspectral imaging). PLSR and different classifiers (PLSDA, SVM and ANN) were utilized to build the prediction and classification models, respectively. To extract the most influential wavelengths related to the prediction of DMC, the CARS and CSMW techniques were applied. The results indicated that the DMC of two sections on the equator of the potato tuber belly can well represent the DMC of the intact potato, and together with the spectral detection at the equatorial position, it provided good performance. The CARS-PLSR prediction model in transmittance mode showed stronger correlations than other systems, with Rp and RMSEP values of 0.968 and 0.413%, respectively. The CARS-SVM-Linear classification model exhibited the best performance with classification rates of 100% and 97.62% in the training and testing sets, respectively. Moreover, the spectra preferred by CARS and CSMW variable selection methods in transmittance mode overlapped near the absorption peak at 980 nm, indicating the importance of this band for predicting DMC. This study presented the feasible application of using spectroscopy to evaluate the DMC of intact potatoes and classify potatoes based on thresholds that are crucial to consumers and food processors. [ABSTRACT FROM AUTHOR]

Soluble solids content (SSC) and firmness are two important indicators to evaluate the quality of kiwifruit. In this study, an online diffuse reflection spectra acquisition system was designed to realize quality detection of kiwifruit. We try to find the optimized conveying speed by comparing the performance of models with the indicators including the correlation coefficient (r) and the prediction root mean square error (RMSE) of the prediction model. To achieve this goal, diffuse reflection spectra of "Hayward" kiwifruit at 0 m·s− 1 (static), 0.3 m·s− 1 and 0.5 m·s− 1 were collected and the effect of conveying speed on quality prediction models was investigated. Different combinations of modeling methods (i.e., stepwise multiple linear regression (SMLR), partial least squares regression (PLS), principal component regression (PCR)) and pretreatment methods (i.e., internal reference correction (IR), multiple scatter correction (MSC), standard normal variable correction (SNV)) were used to predict SSC and firmness online. The results show that the combination of PLS and IR has the best accuracy on the determination of SSC at the conveying speed of 0.3 m·s− 1 (rc = 0.9156, RMSEC = 0.405 °Brix, rp = 0.9196, RMSEP = 0.437 °Brix). For the determination of kiwifruit firmness, the combination of PCR and IR has the best accuracy at the conveying speed of 0.5 m·s− 1 (rc = 0.8936, RMSEC = 0.287 N, rp = 0.8929, RMSEP = 0.265 N). It proves that the use of specific pretreatment methods and modeling methods can greatly reduce the impact of optical path change, stray light and weak light intensity in the online detection mode, and improve the accuracy and reliability of the online detection model of kiwifruit SSC and firmness. This study can provide a reference for the development of fast and online measurement systems for kiwifruit internal quality. [ABSTRACT FROM AUTHOR]

Fruits and vegetables are very important agricultural products in daily life. Evaluating the quality attributes of fresh fruits and vegetables by nondestructive sensing techniques has been an intensive research topic over the past two decades. The research progress on the detection of internal and external quality attributes of fresh fruits and vegetables using various nondestructive spectroscopic and imaging techniques, including visible/near-infrared spectroscopy, time-resolved and space-resolved spectroscopy, machine vision, hyperspectral and multispectral imaging, fluorescence techniques, X-ray imaging, computed tomography scanning, magnetic resonance imaging, and Raman techniques, is presented and discussed in this review. Each kind of fruit or vegetable shows great variability in physical characteristics (including size, shape, color, and temperature) and biological characteristics (including cultivar, season, maturity level, and geographical origin). This physical and biological variability complicates the quality evaluation of fresh fruits and vegetables. To eliminate the influence of variability and improve the inspection accuracy, a lot of attempts, including pre-processing, light intensity transformation, global model, band math, model transfer, etc., have been made in image correction and spectral compensation methods. This review provides a detailed summary of the various methods for solving the problem of physical and biological variability, as well as their advantages and disadvantages. Additionally, the current problems to be solved in spectroscopic and imaging technologies and the research trends of nondestructive measurement of the quality of fresh fruits and vegetables in the future are also revealed. [ABSTRACT FROM AUTHOR]