Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley

Type I MADS-box transcription factor TaMADS-GS regulates grain size by stabilizing cytokinin signalling during endosperm cellularization in wheat

Grain size is one of the important traits in wheat breeding programs aimed at improving yield, and cytokinins, mainly involved in cell division, have a positive impact on grain size. Here, we identified a novel wheat gene TaMADS-GS encoding type I MADS-box transcription factor, which regulates the cytokinins signalling pathway during early stages of grain development to modulate grain size and weight in wheat. TaMADS-GS is exclusively expressed in grains at early stage of seed development and its knockout leads to delayed endosperm cellularization, smaller grain size and lower grain weight. TaMADS-GS protein interacts with the Polycomb Repressive Complex 2 (PRC2) and leads to repression of genes encoding cytokinin oxidase/dehydrogenases (CKXs) stimulating cytokinins inactivation by mediating accumulation of the histone H3 trimethylation at lysine 27 (H3K27me3). Through the screening of a large wheat germplasm collection, an elite allele of the TaMADS-GS exhibits higher ability to repress expression of genes inactivating cytokinins and a positive correlation with grain size and weight, thus representing a novel marker for breeding programs in wheat. Overall, these findings support the relevance of TaMADS-GS as a key regulator of wheat grain size and weight.

Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation

The flour milling industry-a vital component of global food production-is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.

Enhancing traceability of wheat quality through the supply chain

With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition

Several studies have shown that mealworms (Tenebrio molitor L.) could provide animals and humans with valuable nutrients. Tenebrio molitor larvae were studied to determine whether their rearing diets affected their fat and fatty acid content and to ascertain if it is possible to detect the changes in the larval fat composition using near-infrared reflectance spectroscopy (NIRS). For this reason, a standard control diet (100% wheat bran) and an experimental diet, consisting of wheat bran and the supplementation of a different substrate (coconut flour, flaxseed flour, pea protein flour, rose hip hulls, grape pomace, or hemp protein flour) were used. The results showed lesser weight gain and slower growth rates for larvae raised on diets with a high fat content. A total of eight fatty acids were identified and quantified, where palmitic, oleic, and linoleic acids were the most prevalent and showed a correlation between larval content and their content in the rearing diets. There was a high content of lauric acid (3.2-4.6%), myristic acid (11.4-12.9%), and α-linolenic acid 8.4-13.0%) in mealworm larvae as a result of the high dietary content of these fatty acids. NIR spectra were also influenced by the fat and fatty acid composition, as larval absorbance values differed greatly. The coefficient of the determination of prediction (R²_P) was over 0.97, with an RPD value of 8.3 for the fat content, which indicates the high predictive accuracy of the NIR model. Furthermore, it was possible to develop calibration models with great predictive efficiency (R²_P = 0.81-0.95, RPD = 2.6-5.6) for all fatty acids, except palmitoleic and stearic acids which had a low predictive power (R²_P < 0.5, RPD < 2.0). The detection of fat and fatty acids using NIRS can help insect producers to quickly and easily analyze the nutritional composition of mealworm larvae during the rearing process.

Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt

Due to the superiorities of Styrene butadiene styrene (SBS) modified asphalt, it is widely used in civil engineering application. Meanwhile, accurately predicting and obtaining performance parameters of SBS modified asphalt in unison is difficult. At present, it is essential to discover an accurate and simple method between the input and output data. ANNs are used to model the performance and behavior of materials in place of conventional physical tests because of their adaptability and learning. The objective of this study discussed the application of ANNs in determining performance of SBS modified asphalt, based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) tests. A total of 150 asphalt mixtures were prepared from three matrix asphalt, two SBS modifiers and five modifier dosages. With the most suitable algorithm and number of neurons, an ANN model with seven hidden neurons was used to predict SBS content, needle penetration and softening point by using infrared spectral data of different modified asphalts as input. The results indicated that ANN-based models are valid for predicting the performance of SBS modified asphalt. The coefficient of determination (R²) of SBS content, softening point and penetration prediction models with the same grade of asphalt exceeded 99%, 98% and 96%, respectively. It can be concluded that ANNs can provide well-satisfied regression models between the SBS content and infrared spectrum statistics sets, and the precision of penetration and softening point model founded by the same grade of asphalt is high enough to can meet the forecast demand.

Design of a Phenotypic Sensor About Protein and Moisture in Wheat Grain

A near-infrared (NIR) spectrometer can perceive the change in characteristics of the grain reflectance spectrum quickly and nondestructively, which can be used to determine grain quality information. The full-band spectral information of samples of multiple physical states can be measured using existing instruments, yet it is difficult for the full-band instrument to be widely used in grain quality detection due to its high price, large size, non-portability, and inability to directly output the grain quality information. Because of the above problems, a phenotypic sensor about grain quality was developed for wheat, and four wavelengths were chosen. The interference of noise signals such as ambient light was eliminated by the phenotypic sensor using the modulated light signal and closed sample pool, the shape and size of the incident light spot of the light source were determined according to the requirement for collecting the reflectance spectrum of the grain, and the luminous units of the light source with stable light intensity and balanced luminescence were developed. Moreover, the sensor extracted the reflectance spectrum information using a weak optical signal conditioning circuit, which improved the resolution of the reflectance signal. A grain quality prediction model was created based on the actual moisture and protein content of grain obtained through Physico-chemical analyses. The calibration test showed that the R² of the relative diffuse reflectance (RDR) of all four wavelengths of the phenotypic sensor and the reflectance of the diffusion fabrics were higher than 0.99. In the noise level and repeatability tests, the standard deviations of the RDR of two types of wheat measured by the sensor were much lower than 1.0%, indicating that the sensor could accurately collect the RDR of wheat. In the calibration test, the root mean square errors (RMSE) of protein and moisture content of wheat in the Test set were 0.4866 and 0.2161%, the mean absolute errors (MAEs) were 0.6515 and 0.3078%, respectively. The results showed that the NIR phenotypic sensor about grain quality developed in this study could be used to collect the diffuse reflectance of grains and the moisture and protein content in real-time.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Application of near-infrared handheld spectrometers to predict semolina quality

BACKGROUND

Durum wheat semolina is the best raw material for pasta production and its protein content and gluten strength are essential for cooking quality. The need to develop rapid methods to speed up quality control makes near-infrared spectroscopy (NIR) a useful method that is widely accepted in the cereal sector. In this study, two non-destructive and rapid technologies, a low-cost sensor providing a short wavelength NIR range (swNIR: 700-1100 nm) and a handheld NIR spectrometer (NIR: 1600-2400 nm), were employed to evaluate semolina quality. The spectra data were correlated with chemical (protein content) and rheological parameters (i.e., Gluten Index, Alveograph®, Sedimentation test, GlutoPeak®). A partial least squares (PLS) model was used to compare the efficacy of swNIR and NIR.

RESULTS

The protein content was the reference parameter that correlated best with the spectra data and provided the best regression model (r model = 0.9788 for NIR and 0.9561 for swNIR). GlutoPeak indices also correlated well with spectral data, particularly with swNIR spectra. A provisional multivariate model was applied to classify semolina samples in quality classes by using their spectra. Better modeling efficiency was obtained for swNIR.

CONCLUSION

The results highlighted the advantages of a pocket-sized low cost sensor (swNIR), which is easier to use directly at the sample source than laboratory instruments or more expensive portable devices. © 2020 Society of Chemical Industry.

A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

BACKGROUND

Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified.

RESULTS

Using two different NIR devices, ASD FieldSpec^®3 performed better than Polychromix Phazir™ in improving R² and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750-2500 nm), using a subset of wavelengths (1100-2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content.

CONCLUSIONS

In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts.

NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)

In this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm^-1 region.

Prediction of CP and starch concentrations in ruminal in situ studies and ruminal degradation of cereal grains using NIRS

Ruminal in situ incubations are widely used to assess the nutritional value of feedstuffs for ruminants. In in situ methods, feed samples are ruminally incubated in indigestible bags over a predefined timespan and the disappearance of nutrients from the bags is recorded. To describe the degradation of specific nutrients, information on the concentration of feed samples and undegraded feed after in situ incubation ('bag residues') is needed. For cereal and pea grains, CP and starch (ST) analyses are of interest. The numerous analyses of residues following ruminal incubation contribute greatly to the substantial investments in labour and money, and faster methods would be beneficial. Therefore, calibrations were developed to estimate CP and ST concentrations in grains and bag residues following in situ incubations by using their near-infrared spectra recorded from 680 to 2500 nm. The samples comprised rye, triticale, barley, wheat, and maize grains (20 genotypes each), and 15 durum wheat and 13 pea grains. In addition, residues after ruminal incubation were included (at least from four samples per species for various incubation times). To establish CP and ST calibrations, 620 and 610 samples (grains and bag residues after incubation, respectively) were chemically analysed for their CP and ST concentration. Calibrations using wavelengths from 1250 to 2450 nm and the first derivative of the spectra produced the best results (R 2 Validation=0.99 for CP and ST; standard error of prediction=0.47 and 2.10% DM for CP and ST, respectively). Hence, CP and ST concentration in cereal grains and peas and their bag residues could be predicted with high precision by NIRS for use in in situ studies. No differences were found between the effective ruminal degradation calculated from NIRS estimations and those calculated from chemical analyses (P>0.70). Calibrations were also calculated to predict ruminal degradation kinetics of cereal grains from the spectra of ground grains. Estimation of the effective ruminal degradation of CP and ST from the near-infrared spectra of cereal grains showed promising results (R 2>0.90), but the database needs to be extended to obtain more stable calibrations for routine use.

Determination of hemicellulose, cellulose and lignin content using visible and near infrared spectroscopy in Miscanthus sinensis

Lignocellulosic components including hemicellulose, cellulose and lignin are the three major components of plant cell walls, and their proportions in biomass crops, such as Miscanthus sinensis, greatly impact feed stock conversion to liquid fuels or bio-products. In this study, the feasibility of using visible and near infrared (VIS/NIR) spectroscopy to rapidly quantify hemicellulose, cellulose and lignin in M. sinensis was investigated. Initially, prediction models were established using partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function neural network (RBF_NN) based on whole wavelengths. Subsequently, 23, 25 and 27 characteristic wavelengths for hemicellulose, cellulose and lignin, respectively, were found to show significant contribution to calibration models. Three determination models were eventually built by PLS, LS-SVM and ANN based on the characteristic wavelengths. Calibration models for lignocellulosic components were successfully developed, and can now be applied to assessment of lignocellulose contents in M. sinensis.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Near-Infrared Spectroscopy Assay of Key Quality-Indicative Ingredients of Tongkang Tablets

The objective of this paper is to develop an easy and fast near-infrared spectroscopy (NIRS) assay for the four key quality-indicative active ingredients of Tongkang tablets by comparing the true content of the active ingredients measured by high performance liquid chromatography (HPLC) and the NIRS data. The HPLC values for the active ingredients content of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin in Tongkang tablets were set as reference values. The NIRS raw spectra of Tongkang tablets were processed using first-order convolution method. The iterative optimization method was chosen to optimize the band for Cimicifuga glycoside and 5-O-methylvisamminol, and correlation coefficient method was used to determine the optimal band of calycosin glucoside and hesperidin. A near-infrared quantitative calibration model was established for each quality-indicative ingredient by partial least-squares method on the basis of the contents detected by HPLC and the obtained NIRS spectra. The correlation coefficient R ² values of the four models of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin were 0.9025, 0.8582, 0.9250, and 0.9325, respectively. It was demonstrated that the accuracy of the validation values was approximately 90% by comparison of the predicted results from NIRS models and the HPLC true values, which suggested that NIRS assay was successfully established and validated. It was expected that the quantitative analysis models of the four indicative ingredients could be used to rapidly perform quality control in industrial production of Tongkang tablets.

Headspace Fourier transform infrared spectroscopy for the differentiation of Pandanus species

Headspace Fourier Transform Infrared Spectroscopy (HS-FTIR) in tandem with chemometrics was applied to differentiate several species of the genus Pandanus. The headspace was generated from each Pandanus sample after incubation in a tightly sealed sample chamber. The resulting FTIR spectra of the headspace samples were found to be almost similar, but the application of principal component analysis (PCA) effectively differentiated the species. The unique spectral features for some samples were highlighted in the second-derivative FTIR spectra. A higher variance was exhibited in the PCA bi-plot of the 2nd derivative spectral data. The principal components differentiated not only the species, but also the cultivars or varieties, which formed distinct but proximate clusters. The manner of clustering obtained in this study resembled the behavior reported in a Random Amplified Polymorphic DNA analysis conducted on the Pandanus samples. The results demonstrate the potential of headspace FTIR spectroscopy as a simple, rapid, non-destructive, and relatively inexpensive method to discriminate between plant species and varieties.

Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy

Barley grains are rich in phenolic compounds, which are associated with reduced risk of chronic diseases. Development of barley cultivars with high phenolic acid content has become one of the main objectives in breeding programs. A rapid and accurate method for measuring phenolic compounds would be helpful for crop breeding. We developed predictive models for both total phenolics (TPC) and p-coumaric acid (PA), based on near-infrared spectroscopy (NIRS) analysis. Regressions of partial least squares (PLS) and least squares support vector machine (LS-SVM) were compared for improving the models, and Monte Carlo-Uninformative Variable Elimination (MC-UVE) was applied to select informative wavelengths. The optimal calibration models generated high coefficients of correlation (r_pre) and ratio performance deviation (RPD) for TPC and PA. These results indicated the models are suitable for rapid determination of phenolic compounds in barley grains.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.

Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model

Near-infrared spectroscopy combined with chemometrics was applied to construct a hybrid model for the non-invasive detection of protein content in different types of plant feed materials. In total, 829 samples of plant feed materials, which included corn distillers’ dried grains with solubles (DDGS), corn germ meal, corn gluten meal, distillers’ dried grains (DDG) and rapeseed meal, were collected from markets in China. Based on the different preprocessed spectral data, specific models for each type of plant feed material and a hybrid model for all the materials were built. Performances of specific model and hybrid model constructed with full spectrum (full spectrum model) and selected wavenumbers with VIP (variable importance in the projection) scores value bigger than 1.00 (VIP scores model) were also compared. The best spectral preprocessing method for this study was found to be the standard normal variate transformation combined with the first derivative. For both full spectrum and VIP scores model, the prediction performance of the hybrid model was slightly worse than those of the specific models but was nevertheless satisfactory. Moreover, the VIP scores model obtained generally better performances than corresponding full spectrum model. Wavenumbers around 4500 cm^-1, 4664 cm^-1 and 4836 cm^-1 were found to be the key wavenumbers in modeling protein content in these plant feed materials. The values for the root mean square error of prediction (RMSEP) and the relative prediction deviation (RPD) obtained with the VIP scores hybrid model were 1.05% and 2.53 for corn DDGS, 0.98% and 4.17 for corn germ meal, 0.75% and 6.99 for corn gluten meal, 1.54% and 4.59 for DDG, and 0.90% and 3.33 for rapeseed meal, respectively. The results of this study demonstrate that the protein content in several types of plant feed materials can be determined using a hybrid near-infrared spectroscopy model. And VIP scores method can be used to improve the general predictability of hybrid model.

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of spectra of the jujube (Zizyphus jujuba Mill.) fruit samples from four geographical regions. Prediction models were developed for the quantitative prediction of the contents of jujube fruit, i.e., total sugar, total acid, total phenolic content, and total antioxidant activity. Four pattern recognition methods, principal component analysis (PCA), linear discriminant analysis (LDA), least squares-support vector machines (LS-SVM), and back propagation-artificial neural networks (BP-ANN), were used for the geographical origin classification. Furthermore, three multivariate calibration models based on the standard normal variate (SNV) pretreated NIR spectroscopy, partial least squares (PLS), BP-ANN, and LS-SVM were constructed for quantitative analysis of the four analytes described above. PCA provided a useful qualitative plot of the four types of NIR spectra from the fruit. The LS-SVM model produced best quantitative prediction results. Thus, NIR spectroscopy in conjunction with chemometrics, is a very useful and rapid technique for the discrimination of jujube fruit.

Quantitation of α-Lactalbumin by Liquid Chromatography Tandem Mass Spectrometry in Medicinal Adjuvant Lactose

Lactose is a widely used pharmaceutical excipient, sometimes irreplaceable. Traces of residual proteins left during production of lactose are potential allergen to body. The present paper describes a sensitive and specific LC-MS method for the determination of α-lactalbumin (α-La) in lactose samples. Chromatographic separation was performed on an Acquity UPLC BEH300 C18 column (2.1 × 150 mm, 1.7 μm) with an isocratic mobile phase consisting of water containing 0.1% TFA and acetonitrile containing 0.1% TFA (80 : 20, v/v). Mass spectrometric detection was achieved by a triple-quadrupole mass spectrometer equipped with an ESI interface operating in positive ionization mode. Quantitation was performed using selected ion monitoring of m/z 2364 for α-La. The calibration curve was linear from 0.2 to 10 µg/mL. The intra- and interday precisions were less than 7.6% and the accuracy ranged from 96.4 to 104.5%. The limit of quantification (LOQ) was 0.15 µg/mL and the limit of detection (LOD) was 0.05 µg/mL. This method was then successfully applied to investigate 6 different lactose samples. The application can provide technical preparation for the development of specification of lactose.