An effective moisture interference correction method for maize powder NIR spectra analysis.

The detection of maize starch content is of great significance for maize processing industry and near-infrared spectroscopy (NIRS) is an ideal rapid detection technology. However, the interference of moisture in maize is a bottleneck problem that affects the accuracy of NIRS quantitative analysis. In this study, we proposed methods based on external parameter orthogonalization (EPO) combined with wavelength selection algorithms to bring more accurate analytical results. Two groups of maize starch samples with different moisture content distributions were investigated to compare the predictive performance of NIRS models. The results showed that the model built using EPO combined with the synergy interval partial least squares (EPO-siPLS) algorithm exhibited the superior prediction accuracy, whose RMSEP/RMSEPck is improved by 9.7 % compared with that of siPLS model, 25.3 % compared with that of EPO-PLS, and 45.8 % compared with that of the PLS model. This study provides a more accurate and robust new method for rapid detection of maize starch and offers new insights for its application.

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy.

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

The transcription factors ZmNAC128 and ZmNAC130 coordinate with Opaque2 to promote endosperm filling in maize.

Endosperm filling in maize (Zea mays), which involves nutrient uptake and biosynthesis of storage reserves, largely determines grain yield and quality. However, much remains unclear about the synchronization of these processes. Here, we comprehensively investigated the functions of duplicate NAM, ATAF1/2, and CUC2 (NAC)-type transcription factors, namely, ZmNAC128 and ZmNAC130, in endosperm filling. The gene-edited double mutant zmnac128 zmnac130 exhibits a poorly filled kernel phenotype such that the kernels have an inner cavity. RNA sequencing and protein abundance analysis revealed that the expression of many genes involved in the biosynthesis of zein and starch is reduced in the filling endosperm of zmnac128 zmnac130. Further, DNA affinity purification and sequencing combined with chromatin-immunoprecipitation quantitative PCR and promoter transactivation assays demonstrated that ZmNAC128 and ZmNAC130 are direct regulators of 3 (16-, 27-, and 50-kD) γ-zein genes and 6 important starch metabolism genes (Brittle2 [Bt2], pullulanase-type starch debranching enzyme [Zpu1], granule-bound starch synthase 1 [GBSS1], starch synthase 1 [SS1], starch synthase IIa [SSIIa], and sucrose synthase 1 [Sus1]). ZmNAC128 and ZmNAC130 recognize an additional cis-element in the Opaque2 (O2) promoter to regulate its expression. The triple mutant zmnac128 zmnac130 o2 exhibits extremely poor endosperm filling, which results in more than 70% of kernel weight loss. ZmNAC128 and ZmNAC130 regulate the expression of the transporter genes sugars that will eventually be exported transporter 4c (ZmSWEET4c), sucrose and glucose carrier 1 (ZmSUGCAR1), and yellow stripe-like2 (ZmYSL2) and in turn facilitate nutrient uptake, while O2 plays a supporting role. In conclusion, ZmNAC128 and ZmNAC130 cooperate with O2 to facilitate endosperm filling, which involves nutrient uptake in the basal endosperm transfer layer (BETL) and the synthesis of zeins and starch in the starchy endosperm (SE).

Data fusion strategy based on ultraviolet-visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water.

Chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) are the key parameters to reflect the degree of surface water pollution. Ultraviolet - visible (UV-Vis) spectroscopy and near - infrared (NIR) spectroscopy are ideal techniques for rapid monitoring of these indicators. In this study, a strategy based on the fusion of UV-Vis and NIR spectral data (UV-Vis-NIR) for water quality detection was proposed to further improve the quantitative analysis accuracy of spectroscopic methods. Seventy river samples with different levels of pollution were used for spectroscopic analysis. The UV-Vis-NIR fusion spectrum of each water sample was obtained by directly splicing sample's UV-Vis spectrum and NIR diffuse transmission spectrum. The UV-Vis-NIR fusion models were optimized through using different variable selection algorithms. The results show that the UV-Vis-NIR fusion models for surface water COD, AN and TN achieves better prediction results (the root mean square errors of prediction are 6.95, 0.195, and 0.466, respectively) than single-spectroscopic based models. Since better prediction performances were shown under different optimization conditions, the robustness of fusion models were also better than the single-spectroscopic based models. Therefore, the data fusion strategy proposed in this study has a promising application prospect for further accurate and rapid monitoring of surface water quality.

Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN.

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.

Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy.

The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among a set of 143 hybrid indica rice varieties with different eating qualities. The single-grain amylose content (SGAC) and single-grain protein content (SGPC) models were established with coefficients of determination (R2) of 0.9064 and 0.8847, respectively, and the dispersion indicators (standard deviation, variance, extreme deviation, quartile deviation, and coefficient of variation) were proposed to analyze the variations in the SGAC and SGPC based on the predicted results. Our correlation analysis found that the higher the variation in the SGAC and SGPC, the lower the eating quality of the hybrid indica rice. Moreover, the addition of the dispersion indicators of the SGAC and SGPC improved the R2 of the eating quality model constructed using the composition-related physicochemical indicators (amylose content, protein content, alkali-spreading value, and gel consistency) from 0.657 to 0.850. Therefore, this new method proved to be useful for identifying high-eating-quality hybrid indica rice based on single near-infrared spectroscopy prior to processing and cooking.

Characteristics of Fungal Communities and Internal Mildew Occurrence during the Stages of Planting and Storing of Sunflower Seed in China.

Internally mildewed sunflower seeds pose a significant risk to human health. To control internal mildew, it is imperative to study its source in the main production area of China, which has been little investigated. Here, high-throughput sequencing was used to characterize the fungal and fungus-seed communities. Alpha diversity and ANOSIM analyses showed mildew did not alter the fungal compositions significantly. STAMP analysis showed that the sunflower seeds were most vulnerable to internal mildew during the field-planting stage. Alternaria was the predominant mildew-causing pathogen of sunflower seeds for consumption, which may originate from seed transmission and colonize at the seed-development stage. Finally, only a few seeds developed internal mildew with a worrisome level of Alternaria contamination in the humid field climate. NMDS analysis showed that climatic factors also played important roles in shaping microbial change during storage, with a relative humidity (RH) of 67% being the critical threshold in normal-temperature warehouses. Internal mildew never occurred below the RH threshold for the microbial community structure, which hardly changed after an average storage duration. The results indicated that a combination of field management to combat Alternaria, pretreatment with 5 KGy γ-irradiation and drying at the time of storage will minimize or prevent internal mildew. This work also provides an empirical framework for studies of mildewing in other shelled seeds.

Data fusion of near-infrared diffuse reflectance spectra and transmittance spectra for the accurate determination of rice flour constituents.

The data fusion method effectively fuses multiple complementary inputs for highly accurate analysis. The spectral signals collected by near-infrared diffuse reflectance (NIRr) and diffuse transmission (NIRt) contain various information on the physical structure and chemical composition of the sample. Thus, the data fusion method (for NIRr and NIRt) can be used to further improve the accuracy of the NIR quantitative analysis method. The NIR spectroscopic analysis of protein content (PC), amylose content (AC), and fat content (FC) of rice can be used to select high-quality rice varieties. The data obtained using the NIR spectroscopic analysis method for rice flour were used to optimize NIRr and NIRt data fusion and verify the feasibility of this method to achieve more accurate quantitative analysis. Two types of rice flour spectra, NIRr spectra and NIRt spectra, were processed by different pretreatment methods to obtain high-quality fused spectra. The combinations of different pretreatment methods and spectral ranges were subsequently used for the optimization and calibration of partial least square models. The results reveal that the models of the fused spectra processed by the first derivative [NIRr-NIRt (1 der)] exhibit optimal prediction accuracy. The root mean square errors of prediction (RMSEPs) of the optimal NIRr-NIRt (1 der) PC, AC, and FC models were 0.280, 1.240, and 0.165, respectively, which were lower than those of the NIRr and NIRt models. The results show that the fusion of NIRr and NIRt data can achieve accurate detection of rice flour constituents, indicating the method has potential for further development and application.

A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling.

We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms-BOSS, VCPA, iVISSA and IRF-are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/75828-a-variable-selection-method.

A correlation-analysis-based wavelength selection method for calibration transfer.

Considering that the spectral signals vary among different instruments, calibration transfer is required for further popularization and application of the near-infrared spectroscopy (NIRS). To achieve good calibration transfer results, spectral variables with stable and consistent signals between instruments and containing the target component information should be selected. In this study, a correlation-analysis-based wavelength selection method (CAWS) is proposed for calibration transfer. This method relies on the selection of wavelengths at which the spectral responses of master and slave instruments are well correlated (high absolute values of Pearson's correlation coefficient (|Ri|)). The proposed CAWS method was applied to two available datasets, corn and rice bran, and its calibration transfer performances were compared with other wavelength selection methods. The effects of pretreatment methods and calibration transfer algorithms were also assessed. The CAWS optimized models obtained lower root mean square errors of prediction (RMSEPtrans) after calibration transfer, suggesting that the proposed method is capable of effectively improving the efficiency of calibration transfer. Combinations of this method with other wavelength selection methods and calibration transfer algorithms may further enhance the efficiency of calibration transfer, and thus should be thoroughly investigated.

A calibration transfer optimized single kernel near-infrared spectroscopic method.

Single kernel near-infrared spectroscopy (SKNIRS) could aid in the quality screening of early-generation seeds, to improve the efficiency of seed breeding. However, the application of SKNIRS is limited due to the irregular physical characteristics, the heterogeneous constituent distributions of individual seeds, and the insufficient detection accuracy of the reference method. The reported near-infrared detection results of single seeds are often less accurate than those of dehusked seeds and seed flour. In this paper, a calibration transfer-optimized single kernel near-infrared spectroscopic method is proposed. This method aims to accurately detect the chemical composition of single seeds by using the calibration model of the corresponding dehusked seeds or seed flour. The proposed method was applied to the analysis of the protein content of a single rice kernel. The near-infrared transmission spectra of three forms of rice (single rice kernel (SRK), single brown rice kernel (SBK) and rice flour (RF)) of 201 individual rice seeds and the corresponding protein content values were obtained. By comparing different pretreatment methods and spectral ranges, the spectral range 950-1250 nm, the standard normal variate transformation (SNV) pretreatment, and 9 PLS factors were selected to construct the optimal partial least squares (PLS) regression models. Then, the protein content of single rice kernels were determined through two different methods: (i) the direct method, in which single rice kernels were analyzed using the single rice kernel model directly; and (ii) the proposed method, in which the spectra of single rice kernels were transferred into the spectra of single brown rice kernels and rice flours with a calibration transfer algorithm, spectral space transformation (SST), and were analyzed by the respective calibration models. The external validation coefficient correlation (R) value of the direct method was 0.971, and the R values of the proposed method were 0.962 (SBK) and 0.975 (RF). The root mean square error of prediction (RMSEP) value of the direct method was 0.423, and the RMSEP of the proposed method were 0.480 (SBK) and 0.401 (RF). In addition, the transfer results among the spectra of three forms of rice were compared. By comparison, the results of the proposed method are fairly close to the results of the direct method. The results indicate that the spectra generated from one individual rice seed can be transferred freely among the three forms by means of calibration transfer. The proposed method is a promising way to overcome the challenges associated with analyzing individual seeds and to improve SKNIRS.