•A Hg (Ar) lamp was used to correct wavelength of two developed Vis/NIR devices.•Model transfer methods were compared for apple SSC detection.•PDS was recommended for PLS model transfer after ...wavelength correction.•S/B was further used to correct SSC predictions for the independent validation set.
Calibration transfer is an important step for practical applications of Visible and Near-infrared (Vis/NIR) instruments, making the developed model transferable and avoiding recalibration. A calibration transfer method between two developed portable Vis/NIR devices (master and slave devices) for predicting soluble solids content (SSC) of apples was investigated in this study. The partial least squares (PLS) calibration models based on the spectra of the master and the slave devices in the range of 550–930 nm yielded high prediction performance, with the correlation coefficient (Rp) and the root mean square error of the prediction set (RMSEP) of 0.918, 0.552 % and 0.881, 0.666 %, respectively. However, the direct use of the PLS model built by the master instrument to the slave instrument was impracticable. A Hg (Ar) lamp was used to correct the spectral dimension for the two devices, followed by the transfer performance comparison of three methods including piecewise direct standardization (PDS), spectral space transformation (SST), and calibration model transformation based on canonical correlation analysis (CTCCA). The prediction results indicated that PDS yielded better performance when the window size was 3 and the number of the transfer samples was 25, with Rp and RMSEP of 0.874 and 0.713 %, respectively. Lower spectral angle θ¯ and higher spectral correlation coefficient r¯ also illustrated that PDS had a preferable performance compared with SST and CTCCA.After PDS and slope/bias (S/B), the SSC was successfully predicted, achieving high accuracy of Rp = 0.926 and RMSEP = 0.778 %. The above results illustrated that the proposed algorithm was a promising calibration transfer method from the master device to the slave device, and could effectively compensate for the differences of spectral response between the developed Vis/NIR devices and different batches of samples.
In this paper, the influence of variation of spectrum measurement position on the near-infrared (NIR) spectroscopy analysis of soluble solids content (SSC) of apple was studied. The spectra were ...collected around stem, equator and calyx positions for each apple. Partial least squares (PLS) was used to develop compensation models of SSC for each measurement position separately (local position models) and for the full data set containing all positions (global position model). The results indicated that the influence of measurement position on the spectra affected the prediction accuracy of SSC. Compared with the local position models, the global position model was well suited to control the prediction accuracy of the calibration model for SSC with respect to the variation of spectrum measurement position. Next, competitive adaptive reweighted sampling (CARS) was used for the robust global position model to select the most effective wavelengths (EWs). It indicated that the global model established with effective wavelengths (EWs-global position model) achieved more promising results, with rp and RMSEP values for three measurement positions being 0.977, 0.977, 0.955 and 0.409, 0.386, 0.486 °Brix, respectively. Moreover, the local position models based on these effective variables (EWs-local position models) were more accurate than the models built with full range spectrum. The overall results indicated that the EWs-global position model could make the variation of spectrum measurement position a negligible interference for SSC prediction.
•Effect of spectrum measurement position on NIR model of apple SSC was studied.•The spectrum measurement position affected the prediction accuracy of SSC.•The Global model based on effective wavelengths was robust.•A potential method for eliminating the effect of the spectrum measurement position.
Appearance is a very important sensory quality attribute of fruits and vegetables, which can influence not only their market value, consumer's preferences and choice but also their internal quality ...to some extent. External quality of fruits and vegetables is generally evaluated by considering their color, texture, size, shape, as well as the visual defects. External quality inspection of fruits and vegetables manually is a time-consuming and labor intensive work. Over the past decades, computer vision systems, including traditional computer vision system, hyperspectral computer vision system, and multispectral computer vision system, have been widely used in the food industry, and proved to be scientific and powerful tools for the automatic external quality inspection of food and agricultural products. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of computer vision technique in the field of external quality inspection of fruits and vegetables. This paper presents a detailed overview of the comparative introduction, latest developments and applications of computer vision systems in the external quality inspection of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this paper.
•A comparative introduction about the computer vision techniques was reviewed.•The basic components, principles, and developments of computer vision were presented.•The data processing and analytical methods were introduced.•Applications of computer vision in the inspection of external quality were reviewed.
Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are ...still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.
Over the past decades, imaging and spectroscopy techniques have been developed rapidly with widespread applications in non-destructive agro-food quality determination. Seeds are one of the most ...fundamental elements of agriculture and forestry. Seed viability is of great significance in seed quality characteristics reflecting potential seed germination, and there is a great need for a quick and effective method to determine the germination condition and viability of seeds prior to cultivate, sale and plant. Some researches based on spectra and/or image processing and analysis have been explored in terms of the external and internal quality of a variety of seeds. Many attempts have been made in image segmentation and spectra correction methods to predict seed quality using various traditional and novel methods. This review focuses on the comparative introduction, development and applications of emerging techniques in the analysis of seed viability, in particular, near infrared spectroscopy, hyperspectral and multispectral imaging, Raman spectroscopy, infrared thermography, and soft X-ray imaging methods. The basic theories, principle components, relative chemometric processing, analytical methods and prediction accuracies are reported and compared. Additionally, on the foundation of the observed applications, the technical challenges and future outlook for these emerging techniques are also discussed.
•Recent advances in emerging techniques for seed viability were reviewed.•Analytical methods in seed quality and vigor evaluation were briefly described.•Multivariate regression methods for data processing were compared.•Technical challenges and future outlook for emerging techniques were presented.
Soluble solid content (SSC) in fruit is one of the most crucial internal quality factors, which could provide valuable information for commercial decision-making. Near-infrared (NIR) technique has ...effective potentials for determining the SSC since NIR was sensitive to the concentrations of organic materials. In this study, a novel NIR technique, long-wave near infrared (LWNIR) hyperspectral imaging with a spectral range of 930–2548 nm, was investigated for measuring the SSC in pear, which has never been examined in the past. A new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) was proposed to select most effective variables from LWNIR hyperspectral data. The selected variables were used as the inputs of partial least square (PLS) to build calibration models for determining the SSC of ‘Ya’ pear. The results indicated that calibration model built using MC-UVE-SPA-PLS on 18 effective variables achieved the optimal performance for prediction of SSC comparing with other developed PLS models (MC-UVE-PLS and SPA-PLS) by comprehensively considering the accuracy, robustness, and complexity of models. The correlation coefficients between the predicted and actual SSC were 0.88 and 0.88 and the root mean square errors were 0.49 and 0.35 °Brix for calibration and prediction set, respectively. The overall results indicated that long-wave near infrared hyperspectral imaging incorporated to MC-UVE-SPA-PLS model could be applied as an alternative, fast, accurate, and nondestructive method for the determination of SSC in pear.
Moisture content (MC) is one of the important indexes to evaluate maize seed quality. Its accurate prediction is very challenging. In this study, the long-wave near-infrared hyperspectral imaging ...(LW-NIR-HSI) system was used, and the embryo side (S1) and endosperm side (S2) spectra of each maize seed were extracted, as well as the average spectrum (S3) of both being calculated. The partial least square regression (PLSR) and least-squares support vector machine (LS-SVM) models were established. The uninformative variable elimination (UVE) and successive projections algorithm (SPA) were employed to reduce the complexity of the models. The results indicated that the S3-UVE-SPA-PLSR and S3-UVE-SPA-LS-SVM models achieved the best prediction accuracy with an RMSEP of 1.22% and 1.20%, respectively. Furthermore, the combination (S1+S2) of S1 and S2 was also used to establish the prediction models to obtain a general model. The results indicated that the S1+S2-UVE-SPA-LS-SVM model was more valuable with Rpre of 0.91 and RMSEP of 1.32% for MC prediction. This model can decrease the influence of different input spectra (i.e., S1 or S2) on prediction performance. The overall study indicated that LW-HSI technology combined with the general model could realize the non-destructive and stable prediction of MC in maize seeds.
Predicting the soluble solid content (SSC) of peaches based on visible/near infrared spectroscopy has attracted widespread attention. Due to the anisotropic structure of peach fruit, spectra ...collected from different orientations and regions of peach fruit will bring variations in the performance of SSC prediction models. In this study, the effects of spectra collection orientations and regions on online SSC prediction models for peaches were investigated. Full transmittance spectra were collected in two orientations: stem-calyx axis vertical (Orientation1) and stem-calyx axis horizontal (Orientation2). A partial least squares (PLS) method was used to evaluate the spectra collected in the two orientations. Then, each peach fruit was divided into three parts. PLS was used to evaluate the corresponding spectra of combinations of these three parts. Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). Both orientations were ideal for spectra acquisition. Regions without peach pit were ideal for modeling, and the effective wavelengths selected by the SPA led to better performance. The correlation coefficient and root mean square error of validation of the optimal models were 0.90 and 0.65%, respectively, indicating that the optimal model has potential for online prediction of peach SSC.
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