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The effect of variation of fruit orientation on online prediction of soluble solids content (SSC) of ‘Fuji’ apples based on visible and near-infrared (Vis/NIR) spectroscopy was studied. The diffuse reflectance spectra in 550–950 nm were collected with a designed online system in six orientations: stem-calyx axis vertical with stem upward (T1) and stem downward (T5), 45° between stem-calyx axis and horizontal with stem slope upward (T2) and stem slope downward (T4), stem-calyx axis horizontal with stem towards computer side lights (T3), stem-calyx axis horizontal with stem towards belt movement direction (T6). The 180 samples with SSC range of 8.00–13.60°Brix were divided into 135 of calibration set with 1.09 standard deviation (S.D.) and 45 of prediction set with 0.85 S.D. The signal-to-noise ratio (SNR) and area change rate (ACR) were used to evaluate the stability of collected spectra. After the comparison of different preprocessing methods, partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to develop compensation models of SSC for each orientation separately (local models) and all orientations (global model), respectively. Finally, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and their combination were used to select the effective wavelengths (EWs), respectively. Results showed that T1 performed better for our system and influence of measurement orientation on spectra greatly affected SSC prediction accuracy. Comparatively, global model was insensitive to fruit orientation variation. 37 EWs selected by CARS-SPA-PLS model after Savitzky-Golay smoothing in all orientations achieved better results with rp and RMSEP of 0.815, 0.818, 0.837, 0.731, 0.807, 0.842 and 0.487, 0.484, 0.460, 0.573, 0.497, 0.453°Brix, respectively. Generally, global model with EWs could be promisingly used for online SSC prediction of apple.

Maize is an important food crop in the world and it is used in many fields. The classification of maize seed maturity is of great value because it could increase the yield. In this study, near-infrared hyperspectral imaging (NIR-HSI) was employed to explore the maturity classification of maize seeds. In order to observe the influence of spectra of different positions in maize seed for modeling, the hyperspectral images of embryo and endosperm sides of maize seeds were collected in the spectral range of 1000–2300 nm. The average spectra of the embryo side (T1) and endosperm (T2) side were extracted from hyperspectral images, and then, the average spectra of both sides of maize seed (T3) were also calculated. T1, T2 and T3 spectra were used to build calibration models for maturity classification, respectively. And T1 and T2 spectra were imported into these developed classification models, and the classification accuracy of two types of spectra in the model was used to evaluate model applicability. These modeling methods including partial least square discriminant analysis (PLS-DA), decision tree (DT) and adaptive boosting (AdaBoost) methods. The principal component analysis (PCA) was applied to select feature wavelengths, common peaks and valleys in the loading curves of PC1 and PC2 were regarded as feature wavelengths. In order to reduce the influence of division of the calibration set, 50 randomized independent trials were carried out, and the average accuracy and stableness were used to evaluate the performance of models. Comparing among all models, PLS-DA model based on feature wavelengths selected by T2 spectra obtained the optimal results. When T1 and T2 were used as input to the optimal model, the classification accuracy was 98.7% and 100%, respectively. These results demonstrate the potential of the hyperspectral imaging technology for the rapid and accurate classification of maize seed maturity, and the feature wavelengths selected from the endosperm side combined with PLS-DA algorithm could establish a stable model.

Development of the online and nondestructive technologies for inspecting and grading the quality of fruit in the postharvest period can improve industry competitiveness and profitability. The effect of fruit temperature, diameter and weight on online evaluation system of soluble solids content (SSC) of ‘Ya’ pears using visible/near infrared (Vis/NIR) spectroscopy was studied. To establish calibration models, partial least square (PLS) regression and least squares-support vector machine (LS-SVM) were employed in 630–900 nm and two fruit orientations (stem-calyx axis vertical with stem upward (T1), stem-calyx axis horizontal with stem towards belt moving direction (T2)), respectively. After pretreatments of Savitzky-Golay smoothing (SGS), multiplicative scattering correction (MSC), standard normal variate (SNV), and competitive adaptive reweighted sampling (CARS) for effective wavelength (EWs) selection, models were optimized and compared to evaluate calibration strategies. 36 EWs using PLS (rp = 0.89, RMSEP = 0.56) with the consideration of diameter (T1) and 34 EWs using LS-SVM (rp = 0.90, RMSEP = 0.57) with the consideration of temperature and diameter (T2) were finally selected, respectively. The fusion information of temperature and diameter showed beneficial effect and the best prediction results based on the designed online Vis/NIR half-transmittance system after MSC and 7-SGS for SSC evaluation of pears using LS-SVM, which would be effective to simplify models and promote computing efficiency and further make this proposed nondestructive detection technique have the practical application.

Rapid, non-destructive and reliable detection of starch content in single seed is significant to facilitate the breeding of high-starch corn but difficult for a traditional method of seed composition analysis. This study investigated the possibility of using near-infrared (NIR) hyperspectral imaging technology to determine the starch content in a single kernel corn seed. The hyperspectral images including embryo-up and embryo-down orientations of a corn seed were acquired with a range of 930–2500 nm. The characteristic spectrum of each corn seed was calculated by averaging the two sides’ spectra. All spectra were preprocessed by the smoothing and derivative algorithm, and then, the characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) method. The selected wavelengths were used as the inputs to develop partial least squares regression (PLSR) and nonlinear statistical data models with artificial neural networks (ANN) algorithm. The results indicated that the ANN prediction model based on Levenberg-Marquardt algorithm (LMA) was the optimal for starch content determination with correlation coefficient (Rp) of 0.96 and root mean square error of prediction (RMSEP) of 0.98 in prediction sets. Therefore, NIR hyperspectral imaging technology combined with appropriate chemometric analysis can be considered as a useful tool for starch content determination in corn seed at a kernel level. These results can provide a useful reference for rapid and non-destructive detection of other chemical composition in single corn seed.

The feasibility of using multi-cultivar model for non-invasive and accurate determination of firmness in different cultivar of pears was studied based on visible and near-infrared (Vis-NIR) spectrometric technique. A total of 330 samples were prepared for three cultivars of pears including “Cuiguan”, “Huanghua” and “Qingxiang”. Multi-cultivar model and three separate individual-cultivar models were first established and compared using full spectral variables. Multi-cultivar model did better than any one individual-cultivar model for firmness prediction of all pear cultivars. In order to eliminate useless variables and improve the signal/noise ratio, the pretreated full spectra were calculated by different informative variable selection methods. Combination (MC-UVE-SPA) of both Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) was more effective than single MC-UVE or SPA. Based on MC-UVE-SPA, seventeen effective variables were selected from full spectral 1344 variables for firmness analysis of pears. Linear partial least squares (PLS) and non-linear least squares-support vector machine (LS-SVM) models were developed by using effective variables and then were compared. MC-UVE-SPA-LS-SVM model was proved to be optimal in all developed models. Its correlation coefficient for prediction set ( R pre ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were 0.94, 0.91 and 2.93 for “Cuiguan” pear, 0.93, 0.92 and 2.72 for “Huanghua” pear and 0.92, 0.96 and 2.55 for “Qingxiang” pear, respectively. The overall results indicated that MC-UVE-SPA was a powerful tool to select the effective variables, and MC-UVE-SPA-LS-SVM is simple and excellent for the determination of firmness of three cultivars of pears.

The moisture content (MC) of cucumber seeds was detected nondestructively using two hyperspectral imaging (HSI) systems with complementary spectral ranges. The mean spectrum of each cucumber seed was extracted from hyperspectral images in 400–1000 and 1050–2500 nm separately and it was found that the reflectance spectra decreased as the MC increased in 1050–2500 nm. Calibration models were established by partial least squares regression (PLS) to analyze the predictive ability of preprocessing and wavelength selection methods. The spectra in 400–1000 nm pretreated by Savitzky–Golay smoothing and standard normal variate (SG–SNV) and the 1050–2500 nm spectra pretreated by SG-normalization yielded better results. The optimal wavelengths were obtained by three effective wavelength selection methods, i.e., competitive adaptive reweighted sampling (CARS), iteratively retains informative variables (IRIV), and random frog (RF). Subsequently, the simplified models were built by the selected wavelengths separately. Compared to other developed models, the calibration model established with eight wavelengths chosen by RF from hyperspectral images at 1050–2500 nm achieved optimal performance. The correlation coefficient of prediction (Rpre) was 0.917 and the root mean square error of prediction (RMSEP) was 1.656%. Finally, the visualization of MC distribution was generated at the pixel level. The obtained results in this work indicated that applying HSI technology to measure MC in cucumber seeds was feasible, and the spectrum in 1050–2500 region was more promising than 400–1000 for MC detection. The visualization of MC distribution provided by HSI ensured comprehensive evaluation of MC in single seed level. The selected wavelengths were useful for building a multispectral imaging system to detect MC of cucumber seeds, which could get rid of the seeds with high MC and avoid seed deterioration during storage quickly.

Soluble solids content (SSC) is one of the most important quality attributes affecting the price of fresh fruit. The individual-cultivar model is the most common SSC analysis model. However, this type of model is not the optimal for assessment of SSC in the different cultivars of fruit. In this study, the feasibility of using multi-cultivar model for quantitatively determining SSC in three cultivars of pears was observed based on visible-NIR spectroscopy. The multi-cultivar and individual-cultivar models were developed and different variable selection algorithms were used to optimize models. Results showed that the multi-cultivar model was superior to individual-cultivar models for SSC prediction of all samples and competitive adaptive reweighted sampling (CARS) did better than Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) for selection of effective variables. Based on the selected variables, CARS-PLS and CARS-MLR multi-cultivar models can achieve effective prediction for SSC of three cultivars of pears with similar detection accuracy. The coefficients of determination for prediction set ( R P 2 ) and root mean square error of prediction (RMSEP) obtained by these two types of models are 0.90–0.92 and 0.23–0.30 for three cultivars of pears. The overall results demonstrated that it was feasible to accurately determine SSC of different cultivars of pears using the multi-cultivar model, CARS was a powerful tool to select the efficient variables, and CARS-PLS and CARS-MLR were simple and excellent for the spectral calibration.

Higher accuracy for a prediction model is the unremitting pursuit in the field of optics nondestructive technology. The multipoint full-transmittance spectra ranging from 650 to 1000 nm were acquired at a speed of 0.5 m/s using an on-line spectrum measurement system. The combination of mean normalization and 11 points smoothing were selected as the best spectral preprocessing method for removing undesirable signal excursion and light scatters existed in the original spectra. By investigating the interference of transmittance spectral intensity on the prediction accuracy with the method of efficient spectrum optimization proposed in our study, we found that those transmittance spectra with intensity lower than 0.4 at 920 nm were inefficient for SSC prediction. Furthermore, three different variable selection algorithms were used to select characteristic band for further optimizing the prediction model, the best prediction model was built based on 45 variables selected by random flog (RF) and the performance of the best model was Rpre = 0.9043 and RMSEP = 0.4787 respectively. All mentioned above illustrated that efficient spectrum optimization method coupled with variable selection algorithms were useful for improving the accuracy and robust of prediction model.