Coefficient Of Determination In Calibration Research Articles

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost.

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.

Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves.

Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves.

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard–Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.

Rapid assessment of residual solvent content in the TEGDN dual-base propellants by near-infrared reflectance spectroscopy

Excessive residual solvent (RS) levels in triethyleneglycol dinitrate (TEGDN) dual-base propellants can significantly impair combustion performance. This work aimed to develop a rapid and accurate model for detecting the RS content in the TEGDN dual-base propellants using near-infrared (NIR) spectroscopy in the reflectance mode. The optimal wavelength range for modelling, spanning from 1124.9−1230.2 nm and 1335.5–1527.5 nm, was identified based on absorption peaks characteristic of TEGDN dual-base propellant samples and RS. To enhance the quality of the data, we determined optimal window sizes for pre-processing methods: derivative pre-processing and Savitzky-Golay (S-G) smoothing pre-processing. After evaluating the performance of different pre-treatment methods, we found that the model employing multiple scattering corrections (MSC) in conjunction with first-order derivative (FD) pre-processing demonstrated superior results. The partial least squares (PLS) method was used to build the RS model with an optimal number of factors of 6. For the developed RS model, the root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) were 0.019 and 0.024, respectively. The determination coefficient of calibration (Rc2) and the determination coefficient of cross-validation (Rcv2) were 0.968 and 0.952, respectively. In assessing the validation set using the developed model, we observed a root mean square error of prediction (RMSEP) of 0.025 and a determination coefficient of prediction (Rp2) of 0.958. Importantly, the relative error between the predicted values obtained through the NIR method and the measured values from the reference method consistently remained below 2 % under all circumstances. Consequently, the NIR-based RS model developed in this study offers a rapid and efficient means of detecting RS content in TEGDN dual-base propellants, facilitating judgment regarding the qualification of RS content.

Near-infrared spectroscopy (NIRS) for monitoring the nutritional composition of black soldier fly larvae (BSFL) and frass.

The demand for protein obtained from animal sources is growing rapidly, as is the necessity for sustainable animal feeds. The use of black soldier fly larvae (BSFL) reared on organic side streams as sustainable animal feed has been receiving attention lately. This study assessed the ability of near-infrared spectroscopy (NIRS) combined with chemometrics to evaluate the nutritional profile of BSFL instars (fifth and sixth) and frass obtained from two different diets, namely soy waste and customised bread-vegetable diet. Partial least squares (PLS) regression with leave one out cross-validation was used to develop models between the NIR spectral data and the reference analytical methods. Calibration models with good [coefficient of determination in calibration (Rcal 2 ): 0.90; ratio of performance to deviation (RPD) value: 3.6] and moderate (Rcal 2 : 0.76; RPD value: 2.1) prediction accuracy was observed for acid detergent fibre (ADF) and total carbon (TC), respectively. However, calibration models with moderate accuracy were observed for the prediction of crude protein (CP) (Rcal 2 : 0.63; RPD value: 1.4), crude fat (CF) (Rcal 2 : 0.70; RPD value: 1.6), neutral detergent fibre (NDF) (Rcal 2 : 0.60; RPD value: 1.6), starch (Rcal 2 : 0.52; RPD value: 1.4), and sugars (Rcal 2 : 0.52; RPD value: 1.4) owing to the narrow or uneven distribution of data over the range evaluated. The near-infrared (NIR) calibration models showed a good to moderate prediction accuracy for the prediction of ADF and TC content for two different BSFL instars and frass reared on two different diets. However, calibration models developed for predicting CP, CF, starch, sugars and NDF resulted in models with limited prediction accuracy. © 2023 Society of Chemical Industry.

A rapid method to predict type and adulteration of coconut milk by near-infrared spectroscopy combined with machine learning and chemometric tools

Coconut milk is a soft target for adulterators owing to its simplicity of chemical composition. Professionals and consumers want to control the originalitas of coconut milk, while sellers can profit by mixing fresh coconut milk from low-cost products into high-value fresh coconut milk. Non-destructively and rapidly identifying coconut milk classification goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, near-infrared spectra (NIRs) were collected from fresh coconut milk (FCM), instant coconut milk (ICM), and adulterated fresh coconut milk (A-FCM) in order to investigate the prospect of non-invasively discriminating coconut milk type and at the same time predicting the level of A-FCM. Partial least squares (PLS), linear discriminant analysis (LDA), support vector machine (SVM), and multilayer perceptron (MLP) were employed to establish classification and regression models using NIRs. Combining 18 preprocessing types and hyperparameter optimization of individual machine learning algorithms is carried out together and evaluated using 5-folds cross-validation. All algorithms in this study (LDA, SVM, MLP) obtained the same satisfactory results with all the precision, recall, F1-score, and perfect accuracy (100%) to distinguish FCM, ICM, and A-FCM in both calibration and prediction. Regression models using the SVM obtained acceptable results, with a determination coefficient of calibration and prediction all over 0.93, root mean square error of calibration and prediction all below 8.30%, and ratio of prediction to deviation over 3.80. Last but not least, this study would help apply NIRs to detect the originality of coconut milk in real-world conditions.

Detection of the Adulteration of Dendrobium Huoshanense with Dendrobium Henanense by UV-Vis-Shortwave Near-Infrared Diffuse Reflectance Spectroscopy Combined with Chemometrics.

Dendrobium huoshanense (DHS) is a classic traditional Chinese medicine (TCM) with distinctive medicinal benefits and great economic worth; nevertheless, because of similar tastes and looks, it is simple to adulterate with less expensive substitutes (such as Dendrobium henanense [DHN]). This work aimed to develop a reliable tool to detect and quantify the adulteration of DHS with DHN by using UV-Vis-shortwave near-infrared diffuse reflectance spectroscopy (UV-Vis-SWNIR DRS) combined with chemometrics. Adulterated samples prepared in varying concentrations (0-100%, w/w) were analyzed with UV-Vis-SWNIR DRS methods. Partial least-square-discriminant analysis (PLS-DA) and partial least-squares (PLS) regression techniques were used for the differentiation of adulterated DHN from pure DHS and the prediction of adulteration levels. The PLS-DA classification models successfully differentiated adulterated and nonadulterated DHS with an over 100% correct classification rate. UV-Vis-SWNIR DRS data were also successfully used to predict adulteration levels with a high coefficient of determination for calibration (0.9924) and prediction (0.9906) models and low error values for calibration (3.863%) and prediction (5.067%). UV-Vis-SWNIR DRS, as a fast and environmentally friendly tool, has great potential for both the identification and quantification of adulteration practices involving herbal medicines and foods. UV-Vis-SWNIR DRS combined with chemometrics can be applied to identify and quantify the adulteration of herbal medicines and foods.

Development of NIRS calibration curves for sugars in baked sweetpotato.

Variability in sugar content between raw and cooked sweetpotato storage roots impact nutritional and dietary importance with implications for consumer preference. High-throughput phenotyping is required to breed varieties that satisfy consumer preferences. Near-infrared reflectance spectroscopy (NIRS) calibration curves were developed for analysing sugars in baked storage roots using 147 genotypes from a population segregating for sugar content and other traits. The NIRS prediction curves had high coefficients of determination in calibration (R2 c) of 0.96 (glucose), 0.93 (fructose), 0.96 (sucrose), and 0.96 (maltose). The corresponding coefficients of determination for cross-validation (R2 cv) were 0.92 (glucose), 0.89 (fructose), 0.96 (sucrose) and 0.93 (maltose) and were similar to the R2 c for all sugars measured. The ratios of the standard deviation of the reference set to the standard error of cross-validation were greater than three for all sugars. These results confirm the applicability of the NIRS curves in efficiently determining sugar content in baked sweetpotato storage roots. External validation was performed on an additional 70 genotypes. Coefficients of determination (r2) were 0.88 (glucose), 0.88 (fructose), 0.86 (sucrose) and 0.49 (maltose). The results were comparable to those found for the calibration and cross-validation in fructose, glucose, and sucrose, but were moderate for maltose due to the low variability of maltose content in the population. NIRS can be used for screening sugar content in baked sweetpotato storage roots in breeding programs and can be used to assist with the development of improved sweetpotato varieties that better meet consumer preferences. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Development of new fruit quality indices through aggregation of fruit quality parameters and their predictions using near-infrared spectroscopy

This study introduces a flexible approach to aggregate several quality-related parameters to provide new fruit quality indices (FQIs) with the aim of evaluating the overall quality of fruits. The calculations were based on the aggregation of selected fruit quality parameters, such as total soluble solids (TSS), titratable acidity (TA), pH, firmness, dry matter (DM), and lipid content. The fruit quality index 1 (FQI1) and fruit quality index 2 (FQI2) were calculated, respectively, as geometric and weighted arithmetic means of the scaled fruit quality parameters. Both calculations resulted in numerical scores corresponding to a quality rank ranging from 0 to 100. For rapid and non-destructive detection, near-infrared (NIR) spectra were calibrated for predictions of the developed indices using partial least squares (PLS). The predictive performance was evaluated based on several metrics, including root mean square errors of calibration (RMSEC) and prediction (RMSEP), coefficients of determination for calibration (R2) and prediction (Q2), the ratio of RMSEP and RMSEC (RP/C) and the ratio of prediction to deviation (RPD). Mango, tangerine, and avocado were used in the demonstration. The FQI evaluations offer flexibility in the number of aggregate parameters, making them applicable to various types of fruits. With the NIR detection, the developed indices resulted in a significant reduction in the measurements of quality-related parameters but still provided the overall quality of the fruit samples.

Predicting Chemical Composition and Apparent Total Tract Digestibility on Freeze-Dried Not Ground Faeces Using Near-Infrared Spectroscopy in Pigs

The present study aimed to compare NIRS results using freeze-dried ground or not ground (FDG or FDNG) faeces to predict faecal chemical composition and apparent total tract digestibility (ATTD) coefficients. Two different batches of pigs were used (n = 20 mixed sex pens/batch; 11 pigs/pen; Duroc × (Large White × Landrace)). The first batch of pigs (B1; 50.1 ± 3.44 kg body weight (BW)) was used at 13 wks of age and the second batch (B2; 87.0 ± 4.10 kg BW) was used at 18 wks of age. For both B1 and B2, pens were assigned to five diets formulated to obtain a control [10.03 MJ of net energy (NE), 160.0 g of crude protein (CP), and 9.5 g of standardized ileal digestive (SID) lysine (Lys) per kg of feed], low protein (132.0 g CP and 7.5 g SID Lys), high protein (188.0 g CP and 11.5 g SID Lys), low energy (9.61 MJ NE/kg), and high energy (10.45 MJ NE/kg) diets. After a 10-day adaptation period, one faecal sample was collected daily from each pen floor during 6 days in both B1 and B2 (n = 120/batch). Faecal samples were freeze-dried and analysed via NIRS as FDNG and FDG faeces. Dry matter (DM), organic matter (OM), CP, gross energy (GE), fat, and ATTD coefficients were analysed/calculated. The NIRS calibrations were evaluated by cross-validation, splitting the data in four random groups, or using the leave-one-out method. For both FDNG and FDG faeces, coefficients of determination for calibration (R2cv) and residual predictive deviation (RPD) values were: close to 0.9 and 3 for DM and CP, 0.7–0.8 and ≥2 for OM and GE, 0.6 and <2 for fat, and 0.54–0.75 and ≤2 for ATTD coefficients, respectively. CP was better predicted using FDG faeces (p < 0.05), while DM and OM ATTD were better predicted using FDNG faeces (p < 0.05). In conclusion, NIRS successfully predicts faeces’ chemical components and ATTD coefficients of nutrients using FDNG or FDG faeces.

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible-Near-Infrared Spectroscopy.

Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (a*) and Chroma (C*) and soluble solids (SSC) content and decreasing values in color of lightness (L*), yellowness/blueness (b*) and Hue angle (h*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration (RCal2) and prediction (RPre2) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.

Development of a screening method for adulteration detection in coconut milk via mid-infrared spectroscopy: A study of linear and nonlinear regression method

In the present study, we developed a screening method for detection of adulteration in coconut milk via mid-infrared spectroscopy. Linear and nonlinear regression methods (principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR)) were employed and compared to achieve an optimal screening method. Spectral data were scanned using the FTIR benchtop with a wavelength range of 4000–16702 nm. The calibration models of the linear and nonlinear regression methods were developed using the leave-one-out cross-validation method before testing using predictive data that had been prepared. Furthermore, five spectral data treatment techniques were employed to improve the accuracy of the proposed calibration model. The results obtained show that the SVMR method is better than PCR and PLSR for the detection of adulteration in coconut milk by mid-infrared data spectroscopy. The coefficient of determination for calibration (R2c) and prediction (R2p), the root mean square error of calibration (RMSEC) and prediction (RMSEP) and the ratio of prediction to deviation (RPD) using the SVMR method were 100%, 0.81, 98.40%, 0.87 and 7.86, respectively. Furthermore, based on RPD analysis, it is known that the SVMR model can be used to perform excellent quality control of water-adulterated coconut milk.

Real-Time Infrared Spectroscopic Measurement of Natural Moisturizing Factor

Real-Time Infrared Spectroscopic Measurement of Natural Moisturizing Factor

An approach to detecting diphenylamine content and assessing chemical stability of single-base propellants by near-infrared reflectance spectroscopy

Diphenylamine (DPA) as a stabilizer component plays an important role in maintaining the chemical stability of single-base propellants (SBPs). This work investigated the feasibility of rapidly detecting the content of DPA in SBP by near-infrared reflectance spectroscopy (NIRS). The quantitative NIR model was developed by intervals selection, spectral pretreatment and factor number optimization. The optimal spectral intervals were determined to be 1081 nm ∼ 1280 nm and 1378 nm ∼ 1602 nm based on the characteristic spectral peaks of DPA. By comparing the performance of the developed models with different preprocessing methods, the best preprocessing method was standard normal variate transformation (SNV) + de-trending (Dr) + Smoothing. The optimal number of factors was 6 for DPA model. Partial least squares (PLS) regression was used to establish the calibration models of DPA. For the developed model, the determination coefficients of calibration and prediction (Rc2, Rp2) were 0.9907 and 0.9884, respectively. The root mean square errors of calibration and prediction (RMSEC, RMSEP) were 0.0310 and 0.0342, respectively. The samples in the prediction set were predicted by the developed model, and the average absolute error of the proposed and reference method was only 0.0265. The developed model can be applied in rapid monitor the content of DPA in SBP. In addition, vieille test have demonstrated that the chemical stability of SBP became worse with the decrease of DPA content. The content of DPA contained in the SBP with qualified chemical stability is not less than 0.8753%. Thus, the developed model can be used to judge whether the chemical stability of SBP is qualified or unqualified.

Near-Infrared Reflectance Spectrophotometry (NIRS) Application in the Amino Acid Profiling of Quality Protein Maize (QPM).

The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to be a fast, cost-effective, and non-destructive method. Thus, this study aimed to develop and apply the NIRS prediction models for quantifying amino acids in biofortified quality protein maize (QPM). Sixty-three (63) QPM maize genotypes were used as the calibration set, and another twenty (20) genotypes were used as the validation set. The microwave hydrolysis system coupled with post-column derivatization with 6-amino-quinoline-succinimidyl-carbamate as the derivatization reagent and the HPLC method were used to generate the reference data set used for the calibration development. The calibration models were developed for essential and non-essential amino acids using WINSI Foss software. Good coefficients of determination in calibration (R2cal) of 0.91, 0.93, 0.93, and 0.91 and low standard errors in calibrations (SEC) of 0.62, 0.71, 0.26, and 1.75 were obtained for glutamic acids, alanine, proline, and leucine, respectively, while aspartic acids, serine, glycine, arginine, tyrosine, valines, and phenylalanine had fairly good R2Cal values of 0.86, 0.71, 0.81, 0.78, 0.68, 0.79, and 0.75. In contrast, poor (R2cal) was obtained for histidine (0.07), cystine (0.09), methionine (0.09), lysine (0.20), threonine (0.51), and isoleucine (0.09), respectively. The models’ prediction performances (R2pred) and standard error of prediction (SEP) were reasonably good for certain amino acids such as aspartic acid (0.90), glycine (0.80), arginine (0.94), alanine (0.90), proline (0.80), tyrosine (0.83), valine (0.82), leucine (0.90), and phenylalanine (0.88) with SEP values of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20, and 0.77, respectively. However, certain amino acids had their R2pred below 0.50, which could be improved to become useful for screening purposes for those amino acids. Further work is recommended by including a training set representing the sample population’s variance to improve the model’s performance.

Quickly determination of resorcinol in composite modified double-base propellants based on near-infrared reflectance spectroscopy

Resorcinol, as a stabilizer component, plays an important role in maintaining the stability of composite modified double-base (CMDB) propellants. Near-infrared reflectance spectroscopy (NIRS) was used to quickly detect the resorcinol content of CMDB propellant. The optimal spectral interval was determined to be 4126.92–6927.05 cm−1 by analyzing the useful characteristic peaks in sample spectra. The partial least squares (PLS) algorithm was used to establish the resorcinol prediction models. By comparing the performance of the developed models with different preprocessing, the best method of spectral preprocessing was determined as standard normal variate transformation (SNV) + first-order derivative (1ST). The optimal number of factors was 7 for resorcinol model. For the developed model, the determination coefficients of calibration and cross-validation (Rc2, Rcv2) were 0.997 and 0.987, respectively. The root mean square errors of calibration and cross-validation (RMSEC, RMSECV) were 0.013 %, 0.024 %, respectively. The determination coefficients (Rp2) and the root mean square errors of prediction (RMSEP) of prediction were 0.991 and 0.018 %. The average relative error of the proposed and reference methods was only 0.253 %. The developed method can be used for quickly determining the content of resorcinol in CMDB propellants. In addition, methyl violet test showed that the stability of CMDB propellant became worse with the decrease of resorcinol content. In terms of stability, qualified CMDB propellant should contain resorcinol content not less than 1.285 %. Thus, the prediction model of resorcinol is available for judging the stability of CMDB propellants qualified or unqualified.

Rapid Qualitative and Quantitative Characterization of Arnebiae Radix by Near-Infrared Spectroscopy (NIRS) with Partial Least Squares—Discriminant Analysis (PLS-DA)

Near infrared (NIR) spectroscopy with chemometrics was used for rapid source identification and component quantification of Arnebiae Radix. The modeling parameters were determined using the processing trajectory method. The quantitative model of target components was established using the model fusion method. The root mean square error of calibration (RMSEC), root mean square error of validation (RMSEP), determination coefficient of calibration (Rcal 2 ) and validation (Rpre 2 ), and the ratio of standard error of prediction to standard deviation (RPD) were used to evaluate the constructed model. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of β-β'-dimethylacryloacanin partial least square (PLS) model established using the step-by-step strategy were 1.3004 mg/g, 0.9453, 1.3833 mg/g, 0.9544, and 4.6650, respectively. The RMSEC, Rcal 2, RMSEP, Rpre 2 , and RPD of total pigment PLS model were 4.5095 mg/g, 0.9533, 4.5679 mg/g, 0.9534, and 3.0509, respectively. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of β-β'-dimethylacryloacanin Bagging-PLS model established using the process trajectory were 1.6126 mg/g, 0.9159, 1.2254 mg/g, 0.9649, and 5.2662, respectively. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of the total pigment Bagging-PLS model were 4.3499 mg/g, 0.9565, 4.4662 mg/g, 0.9116, and 3.1205, respectively. The accuracy of quantitative model may be improved using the process trajectory and model fusion method. Partial least squares discriminant analysis (PLS-DA) was used to establish the discriminant models of different sources of Arnebiae Radix. The predicted accuracy rate was 100%. The method is simple, rapid, and demonstrated for the discrimination and quantitative analysis of the main active ingredients of Arnebiae Radix.

Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy

Lentil is an important source of plant-based protein, and the protein and amino acid contents have a significant influence on its nutritional quality and value. In this study, near-infrared reflectance spectroscopy (NIRS) models were developed by partial least squares (PLS) regression to predict the crude protein and 18 amino acid contents of lentil seeds. The effects of sample status (whole and ground), type of spectrometer (PerkinElmer DA 7250 and FT 9700), and amino acid/protein correlation on model performance were analyzed and evaluated. The DA 7250 models and FT 9700 models of protein and 14 amino acids, except histidine, tyrosine, methionine and cysteine, showed good statistical results with coefficients of determination for calibration (R2C) higher than 0.652 and residual predictive deviation (RPD) values higher than 1.57. The DA 7250 models achieved similar accuracy for the determination of compositions in whole and ground samples. Two spectrometers had no significant difference (p > 0.05) for measurement in ground lentils. NIRS models could perform better for certain amino acids when they were highly correlated to protein. Overall, NIRS had a significant potential for rapid, accurate and simultaneous prediction of protein and most amino acid contents in lentils.