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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.

Abstract. Aerosol–fog interactions (AFIs) play pivotal roles in the fog cycle. However, few studies have focused on the differences in AFIs between two successive radiation fog events and the underlying mechanisms. To fill this knowledge gap, our study simulates two successive radiation fog events in the Yangtze River Delta, China, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Our simulations indicate that AFIs in the first fog (Fog1) promote AFIs in the second one (Fog2), resulting in higher number concentration, smaller droplet size, larger fog optical depth, wider fog distribution, and longer fog lifetime in Fog2 than in Fog1. This phenomenon is defined as the self-enhanced AFIs, which are related to the following physical factors. The first one is conducive meteorological conditions between the two fog events, including low temperature, high humidity and high stability. The second one is the feedbacks between microphysics and radiative cooling. A higher fog droplet number concentration increases the liquid water path and fog optical depth, thereby enhancing the long-wave radiative cooling and condensation near the fog top. The third one is the feedbacks between macrophysics, radiation, and turbulence. A higher fog top presents stronger long-wave radiative cooling near the fog top than near the fog base, which weakens temperature inversion and strengthens turbulence, ultimately increasing the fog-top height and fog area. In summary, AFIs postpone the dissipation of Fog1 due to these two feedbacks and generate more conducive meteorological conditions before Fog2 than before Fog1. These more conducive conditions promote the earlier formation of Fog2, further enhancing the two feedbacks and strengthening the AFIs. Our findings are critical for studying AFIs and shed new light on aerosol–cloud interactions.

Soluble solid content (SSC) and acidity (pH) are two important factors indicating the fruit quality of pears and can be measured by near-infrared spectroscopy (NIRS). However, the robustness of these measurements as affected by different origins of pears remains largely unknown. In this study, we developed an NIRS method to measure ‘Dangshan’ pear (Pyrus spp.) SSC and pH and evaluated the robustness of this non-destructive detection method by examining the effects of pears from three different origins in 2019 and 2020. First, the Kennard–Stone method was used to divide the calibration set of the 2020 pear samples from different orchards. The partial least squares (PLS) model was used to establish the local origin and hybrid origin models to predict the pears’ SSC and pH. Second, a combination of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) was implemented to construct spectral prediction models based on effective variables for assessing the pears’ SSC and pH from local and hybrid origins. The results showed that the local origin detection model produced large errors in predicting the SSC and pH of pears from different origins, and the model, established based on the pear samples of three origins, performed better than the local origin and other hybrid origin models. Finally, the model could be effectively simplified using 70 and 52 characteristic variables selected by the CARS method. Pear samples harvested from three different orchards in 2019 were used as an independent set to verify the validity of the selected characteristic variables. The results showed that the predicted R2p for the SSC and pH measurements of pears of three different origins were more than 0.9 and 0.85, respectively. This finding indicates that the difference in the origin of pears has an important influence on the quantitative inversion of pear SSC and pH measurements, and the combination of the hybrid origin model constructed based on the characteristic variables can improve the prediction accuracy. These findings provide an important theoretical basis for the development of rapid detection devices for the measurements of pears’ SSC and pH.

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.

High accuracy on-line estimation for fruit internal quality is still a challenge due to varying geometric structures and orientations. In this study, multiple full-transmittance spectra were collected using short-integration-time mode for each apple. The signal-to-noise ratio of each collected spectrum changed with the measurement position of the fruit due to the heterogeneity of internal composition. To explore the distribution character of transmittance spectra across the apple structure to guide the development of an on-line fruit quality determination system, a methodology which we called ‘zone combination modelling’ was proposed for selecting the most effective spectra for SSC prediction. The orientation of stem–calyx axis vertical was selected as the preferred orientation for quality prediction of ‘Fuji’ apple based on the analysis of the variation and quality of full-transmittance spectra. The most ineffective and most effective zone combinations for SSC prediction were determined by investigating the effect of transmittance spectra within different zone combinations on SSC prediction ability. Ten effective wavelengths selected from the most efficient zone combination were used to develop an optimal prediction model. Results showed that the contribution of different spectral measurement zones to SSC prediction capability varied and that in particular, those collected from the apple core zone should be removed when building SSC prediction models. The coefficient of determination and root mean square errors of prediction and validation sets of SSC, respectively, were 0.733 and 0.61%, 0.721 and 0.71% for the optimal model, indicating that zone combinations model was promising for SSC prediction of apple.

A portable visible and near-infrared (Vis/NIR) device could evaluate and monitor internal qualities of fruit on-tree, as well as during storage conditions after harvest. A portable Vis/NIR device which consisted of a commercial spectrometer in the spectral range of 400–1000 nm, an interactance fibre optic probe, a novel switch system, and a microcontroller, was developed and its ability for apple soluble solids content (SSC) prediction was evaluated. A switch system was designed for spectra collection, resulting in the acquisition of three spectra for each measurement of apple fruit, namely the white reference, dark reference, and sample spectrum, which can be used to correct the spectrum of apple fruit dynamically. The results showed that the dynamic correction was more promising than the static correction in which the reference spectra were obtained only once. A model for SSC was built using partial least square (PLS), with the coefficient of determination of prediction ( R p 2 ), the root mean square error of prediction (RMSEP), and the ratio of the standard deviation of the reference destructive SSC to the RMSEP (RPD) of 0.777, 0.561%, and 2.114, respectively. The model was then embedded in the custom software to make it possible for the portable device to predict SSC of apple directly, followed by validation using independent sets. The validation results gave R p 2 , RMSEP, and RPD of 0.764, 0.672%, 2.029, respectively for SSC prediction under laboratory conditions, and 0.684, 2.777%, and 0.381, respectively for apples on-tree. The prediction results in the field were improved dramatically using the model built by the field data, with R p 2 , RMSEP and RPD of 0.690, 0.604%, and 1.794, respectively. The overall results showed that the developed device had considerable potential to detect the SSC of apple in practical situations.

Moisture content (MC) is one of the important factors to assess the quality of maize seeds. In this study, the feasibility of using long-wave near infrared (LWNIR) hyperspectral imaging (HSI) technique with the spectral range of 930-2548 nm for predicting MC of single maize seeds was observed. The averaged spectra extracted from whole and centroid regions in the embryo side of single maize seeds were pretreated by Savizky-Golay smoothing and first derivative (SG-D1). A combination of uninformative variable elimination (UVE) and successive projections algorithm (SPA) was proposed to select feature wavelengths (variables) from LWNIR hyperspectral data. The quantitative relationship between feature wavelengths and MC was established by partial least square (PLSR) and least square-support vector machine (LS-SVM), respectively. Results illustrated that the UVE-SPA-LS-SVM model established based on spectra of centroid region obtained the best performance for MC detection of the single maize seeds. The correlation coefficient of prediction (Rpre) and root mean square error of prediction (RMSEP) were 0.9325 and 1.2109, respectively. Finally, MC distribution of single maize seed was visualized by pseudo-color map. This study showed LWNIR HSI technique was feasible to measure MC of single maize seeds and a robust and accurate model could be established based on UVE-SPA-LS-SVM method with the spectra of centroid regions.

The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.

Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

On-line estimation of fruit internal attributes based on visible-near infrared spectrum is an effective approach for improving fruit value and farmer income. Brightness correction of the hyperspectral image can significantly affect the accuracy of quality estimation in fruits with spherical geometric structure, while pixel by pixel correction method is time-consuming and impractical in on-line rapid detection. To improve the flexibility and speed of the soluble solids content (SSC) estimation model of intact apple, the spectra of core region, middle region and outer region were extracted from hyperspectral reflectance imaging over the region of 400–1000 nm with a brightness grade segmentation method, and then a multi-region combined partial least square (MCPLS) prediction model was built. Results showed that MCPLS method achieved better results than traditional PLS and multi-region average PLS methods. To further improve the applicability of the prediction model in practice, 21 wavelengths effective for SSC estimation were selected by successive projection algorithm and used to rebuild the prediction model using MCPLS method; the correlation coefficient, root mean square error of prediction set and residual predictive deviation were 0.9132, 0.3929 and 2.1652 respectively. Additionally, multi-region combined method just needs to compute the average spectra of each region, which significantly improved the detection speed by comparison with previous pixel by pixel brightness correction method. Hence the multi-region combined prediction model of SSC was developed based on the spectral contribution of each region to SSC prediction and the method of brightness grade segmentation.

Hyperspectral imaging is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models to extract effective wavelengths for measurement of apple soluble solids content (SSC) based on near infrared (NIR) hyperspectral imaging over the spectral region of 1000–2500 nm. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), random frog (RF), and CARS-SPA, CARS-RF combined algorithms were used for extracting effective wavelengths from hyperspectral images of apples, respectively. Based on the selected effective wavelengths, different models were built and compared for predicting SSC of apple using partial least squares (PLS) and least squared support vector regression (LS-SVR). Among all the models, the models based on the ten effective wavelengths selected by CARS-SPA achieved the best results, with Rp, RMSEP of 0.907, 0.479 °Brix for PLS and 0.917, 0.453 °Brix for LS-SVR, respectively. The overall results indicated that CARS-SPA can be used for selecting the effective wavelengths from hyperspectral data. Both PLS and LS-SVR can be applied to develop calibration models to predict apple SSC. Furthermore, the wavelengths selected by CARS-SPA algorithm has a great potential for online detection of apple SSC.

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.

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. Keywords: Seed viability, NIR, Hyperspectral imaging, Raman spectroscopy, Infrared thermography, Soft X-ray imaging

In addition to other surface quality attributes such as size, color and shape, during sorting of harvested apple fruit, early detection of decay is important due to its infectiousness and potential food safety issue. However, automatic and fast inspection of fruit for decay still remains a major problem for the industry. The use of hyperspectral imaging technique makes it possible to perform detection process automatically. Three spectral regions including Vis-NIR (400–1000 nm), Vis (400–780 nm) and NIR (781–1000 nm) were performed using principal component analysis (PCA) to determine the more effective spectral region and PC vector for distinguishing between sound and decayed tissues. Based on the selected PC, loadings corresponding to each wavelength were analyzed to extract key wavelength images in raw hyperspectral data for multispectral image processing. Two sets of multispectral PC score images from Vis-NIR and NIR regions, respectively, were established. To avoid over-segmentation of traditional standard watershed segmentation, global threshold and Ostu, a novel improved watershed segmentation algorithm based on morphological filtering and morphological gradient reconstruction as well as marking constraint were proposed to segment decayed spots on apples. All samples including 220 decayed and 220 sound fruit were used to assess performance of the proposed algorithm. The classification results indicated that 99% of the decayed fruit and 100% of sound fruit were accurately identified by proposed algorithm based on PC3 score images obtained from multispectral PCA of four key wavelengths in NIR region, respectively. This study demonstrated that multispectral images coupled with the improved watershed segmentation algorithm could be a potential approach for detection of early decay on apples. However, further algorithm optimization is still required obtain higher detection accuracy of decayed apples due to zero tolerance for this type of fruit from consumers and processing industries.

The basic optical properties of fruit tissues are significant and helpful to be known when they are utilized for internal quality detection. The integrating sphere (IS) system was established to measure diffuse reflectance spectra (R') of peel-flesh layer, absorption coefficient (μα) and reduced scattering coefficient (μ's) of flesh layer of `Ya' pear tissues using inverse adding-doubling (IAD) method in 900-1700 nm. Aiming at studying the relationship between soluble solids content (SSC) and optical properties (μα and μ's), partial least square (PLS) regression was employed to establish calibration models based on three wavelength regions. Results showed that better correlations were found using reflectance and μα spectra than using scattering ones with full wavelengths in 900-1350 nm. Moreover, reflectance spectra (rp = 0.8911, RMSEP = 0.4965 °Brix) with 27 effective wavelength (EWs) and μα spectra (rp = 0.8641, RMSEP = 0.5506 °Brix) with 14 EWs both performed satisfactory results using competitive adaptive reweighted sampling (CARS). Good result (rp = 0.8856, RMSEP = 0.5081 °Brix) could also be found by the combination of reflectance and μα spectra with 38 EWs. Results of independent validation test showed that EWs of μα spectra was more promising in pear SSC prediction even though the model stability was not as good as these obtained by reflectance. Therefore, more research needs to be done to improve the stability and accuracy of the IS system. This study illustrated the availability for multiple index quality attributes detection of fruit tissues by detecting reflectance and optical properties information using IS system.

Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.

International audience; The synthesis of (BEDT-TTF)2Cu(NCS)2 in the presence of poly(ethylene glycol) leads to super-organized nanoparticles of 2–8 nm size. Samples contain crystalline nanoparticles of the κ-(BEDT-TTF)2Cu(NCS)2 phase. The electrical conductivity at room temperature is about 0.08 S · cm–1, a typical value for nanopowders of tetrathiafulvalene-based conducting compounds. The current-voltage characteristic for an individual nanoparticle aggregate is fitted with a Shockley diode model. A saturation current of 4.1 pA and a threshold voltage of 0.45 V are extracted. N1s and S2p lines in X-ray photoelectron spectroscopy evidence a charge transfer, characteristic for tetrathiafulvalene-based conducting salts. Magnetic susceptibility studies show a superconducting transition at 9.1 K, a characteristic value for the κ-(BEDT-TTF)2Cu(NCS)2 phase. The thermoelectric power of the nanopowder is represented by the average //c and //b values for the single-crystal. Finally, resistivity for the nanopowder is nearly flat in the metallic region.

Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930–2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full-spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full-spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is Rpre = 0.9311 ± 0.0094 and RMSEP = 1.2131 ± 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed.

As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm

The rapid and non-destructive detection of moisture in withering leaves is an unsolved problem because the leaves are stacked together and have random orientation. To address this issue, this study aimed to establish more robust and accurate models. The performance of front side, back side and multi-region models were compared, and the front side model showed the worst transferability. Therefore, five effective wavelength (EW) selection algorithms were combined with a successive projection algorithm (SPA) to select EWs. It was found that the shuffled frog leaping algorithm (SFLA) combined with SPA was the best method for the front side model for moisture analyses. Based on the selected EWs, the extreme learning machine (ELM) became the model with the best self-verification result. Subsequently, moisture distribution maps of withering leaves were successfully generated. Considering the processing demand of withering leaves, local region models developed based on partial least squares and the SFLA-SPA method were applied to predict the moisture of withering leaves in the local and stacked region. The results showed that the RPD, R

In this study Vis/NIR spectroscopy was applied to evaluate soluble solids content (SSC) of tomato. A total of 168 tomato samples with five different maturity stages, were measured by two developed systems with the wavelength ranges of 500–930 nm and 900–1400 nm, respectively. The raw spectral data were pre-processed by first derivative and standard normal variate (SNV), respectively, and then the effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and random frog (RF). Partial least squares (PLS) and least square-support vector machines (LS-SVM) were employed to build the prediction models to evaluate SSC in tomatoes. The prediction results revealed that the best performance was obtained using the PLS model with the optimal wavelengths selected by CARS in the range of 900–1400 nm (Rp = 0.820 and RMSEP = 0.207 °Brix). Meanwhile, this best model yielded desirable results with Rp and RMSEP of 0.830 and 0.316 °Brix, respectively, in 60 samples of the independent set. The method proposed from this study can provide an effective and quick way to predict SSC in tomato.

The portable device could help to obtain a complete follow-up of fruit quality in orchards and during post-harvest. Thus, it is an important step to develop portable and non-destructive technology for current and future research in fruit. In this study, the ability of portable visible-near infrared (Vis-NIR) spectroscopy to non-invasively determine sugar content in pear was studied. Partial least square regression (PLSR) was applied to establish calibration models based on the spectral signatures of three regions (550–1050, 650–950, 750–1050 nm) and four types of data sets (Set-I, Set-II, Set-III, and Set-IV), respectively, and the performance of models was compared to determine the optimal spectral calibration strategy. The spectral region of 650–950 nm was proved to be much better compared with other two spectral regions. Competitive adaptive reweighted sampling (CARS) algorithm was used to reduce redundancy and collinearity of the original spectral data based on the optimal spectral region for selecting the most important wavelengths. The CARS-PLSR was identified as the most effective method to calibrate the prediction models for sugar content determination, resulting in good coefficient of determination for prediction ( $$ {R}_P^2 $$ ) of 0.85–0.92 and root mean square error of prediction (RMSEP) of 0.27–0.20 for four types of data sets, respectively. The overall results show that the portable Vis-NIR spectroscopy is a promising tool for the non-destructive on-site evaluation of sugar content in pear, as well as affording the additional advantage of low cost.

Early detection of internal bruising is one of the major challenges in blueberry postharvest quality sorting processes. The potential of using near infrared (NIR) hyperspectral reflectance imaging (950–1650 nm) with reduced spectral features was investigated for blueberry internal bruising detection 30 min to 12 h after mechanical impact. A least squares support vector machine (LS-SVM) was used to develop classification models to compute the spatial distribution of bruising based on the spectra extracted from regions of interest (ROIs) at four measurement times (30 min, 2 h, 6 h, and 12 h after mechanical impact). Three feature selection methods were used to select optimum wavelengths or band ratio images for bruising detection. The classification model, developed using optimum wavelengths selected by competitive adaptive reweighted sampling (CARS) (CARS-LS-SVM model) and full spectra (full spectra-LS-SVM), had similar performance in the identification of bruised blueberries. Band ratio images (1235 nm/1035 nm) achieved a comparable accuracy with the CARS-LS-SVM model at 6 h, and higher accuracy than CARS-LS-SVM and full spectra-LS-SVM models at 12 h. The overall classification accuracies of 77.5%, 83.8%, 92.5%, and 95.0% were obtained by band ratio images for blueberries 30 min, 2 h, 6 h, and 12 h after impact, respectively. In order to evaluate the performance of the proposed methods, additional validation samples were processed by the detection algorithm. The overall discrimination accuracies for healthy and bruised blueberries in the validation set were 93.3% and 98.0%, respectively, for CARS-LS-SVM model, and 93.3% and 95.9%, respectively, for band ratio images. The overall results indicated that NIR reflectance imaging can detect blueberry internal bruising as early as 30 min after mechanical impact, and band ratio images computed from two wavelengths showed great potential to detect blueberry internal bruising on the packing line.

Decay is a serious problem in citrus storage and transportation. However, the automatic detection of decayed citrus remains a problem. In this study, the long wavelength near-infrared (LW-NIR) hyperspectra reflectance images (1000–1850 nm) of oranges were obtained, and an effective method to detect decayed citrus was proposed. Three effective wavelength selection algorithms and two classification algorithms were used to build decay detection models in pixel-level, as well as the two-band ratio images, pseudo-color image enhancement and improved watershed segmentation were used to build decay detection models in image-level. The image-level detection method proposed in this study obtained a total success rate of 92% for all fruit, indicating its potential to detect decayed oranges online. Moreover, the LW-NIR hyperspectral reflectance imaging is verified as a useful method to detect surface defects of fruits.

International audience; Sulfur-rich acceptors, birhodanines, 3,3'-dialkyl-5,5'-bithiazolidinylidene-2,2'-dione-4,4'-dithiones (OS-R, R = Et and Pr) and 3,3'-dialkyl-5,5'-bithiazolidinylidene-4,4'-dione-2,2'-dithiones (SO-R, R = Et), as well as the sulfur analogues, 3,3'-dialkyl-5,5'-bithiazolidinylidene-2,4,2', 4'-tetrathiones (SS-R, R = Et, Pr), form 1 1 composition charge-transfer complexes with donors such as pyrene, perylene, and coronene. These complexes have mixed stacks, and the SS-R complexes show n-channel transistor properties due to the intercolumnar S center dot center dot center dot S contacts between the acceptors. By contrast, the OS-R and SO-R complexes exhibit basically hole-dominant ambipolar properties due to the absence of S center dot center dot center dot S contacts. Accordingly, the charge transport is governed mostly by the direct interchain interactions instead of the transport along the columns.

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.

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.

A deep-learning architecture based on Convolutional Neural Networks (CNN) and a cost-effective computer vision module were used to detect defective apples on a four-line fruit sorting machine at a speed of 5 fruits/s. A CNN based classification architecture was trained and tested, with the accuracy, recall, and specificity of 96.5%, 100.0%, and 92.9%, respectively, for the testing set. An inferior performance was obtained by a traditional image processing method based on candidate defective regions counting and a support vector machine (SVM) classifier, with the accuracy, recall, and specificity of 87.1%, 90.9%, and 83.3%, respectively. The CNN-based model was loaded into the custom software to validate its performance using independent 200 apples, obtaining an accuracy of 92% with a processing time below 72 ms for six images of an apple fruit. The overall results indicated that the proposed CNN-based classification model had great potential to be implemented in commercial packing line.

The objective of this study was to improve the detection accuracy of soluble solids content (SSC) of apples by integrating spectra and textural features. The spectral data were directly extracted from the region of interest (ROI) of hyperspectral reflectance images of apples over the region of 400–1000 nm, while the textural features were obtained by a texture analysis conducted on the ROI images based on grey-level co-occurrence matrix (GLCM). A new regression method called combined partial least square (CPLS) was proposed to analyze the integrations of spectra and different kinds of textural features. In this algorithm, the score matrix matrices of the spectral data and textural features were obtained by PLS analysis separately and then used together for calibration. The prediction results indicated that the CPLS model developed with the integration of spectra and correlation feature achieved promising results and improved SSC predictions compared with the spectral data when used alone. Next, stability competitive adaptive reweighted sampling (SCARS) was conducted to select informative wavelengths for SSC prediction. The CPLS model based on the integration of SCARS selected spectra and correlation gave better results than those with the full wavelength range. The correlation coefficient and root mean square errors of prediction set and validation set were 0.9327 and 0.641%, 0.913 and 0.6656%, respectively. Hence, the integration of spectra and correlation extracted from hyperspectral reflectance images, coupled with CPLS and SCARS methods, showed a considerable potential for the determination of SSC in apples.

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.

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.

Fruit skin defects may cause fruit spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored ‘Pinggu’ peaches based on visible-near infrared (vis–NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality to select several wavelengths that could potentially be used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425–780 nm), the near infrared spectrum (781–1000 nm), the full-spectrum (400–1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image ( Q 781/848 ) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio ( Q 781/848 ) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other fruit.