Standard Error Of Cross-validation Research Articles

Correlations for the prediction of higher heating values of liquid fuels from their densities, viscosities, and flash points values

In this study, correlations of the higher heating values (HHV) of three groups of liquid fuels, vegetable oils, alkanes, and alcohols were developed through experimentation and prediction with the aid of the statistical software package MINITAB 16 (PA, USA). The HHVs (in kJ/kg), of the samples were obtained as a function of the density, viscosity, and flash points physical tests. Reliability of models were evaluated through statistical validation and cross-validation standard error of estimate (S), coefficient of determination (R2), predicted residual error of the sum of square statistics (PRESS statistics), error estimations (average bias error, ABE, and AAE, average absolute error), and the residual versus fitted values plot. For the vegetable oils, the best correlation was HHV = -29.5 + 77.8ρ -0.0124μ + 0.00549β (R2 = 79.6%, p-value = 0.147, AAE = 0.515, ABE = 0.005). For alkanes it was: HHV = 66.0322 – 38.3421ρ + 2.9392μ (R2 = 100%, p-value = 0.002, AAE = 0.033, ABE = 7.27 × 10-5). For alcohols; HHV = -100 + 171ρ (R2 = 86.4%, p-value = 0.070, AAE = 4.193, ABE = 0.316). These correlations could be instrumental to achieving maximum productivity with minimal utilization of energy in industry and could be extended to other liquid fuels after minor modification.

White Crystal Cane Sugar Analysis Using a Noninvasive Method for Detection of Tampering with Sand

In this study, a noninvasive and highly analytical frequency method for the detection of tampering with sand (SA) of white crystal cane sugar (WCCS) was developed using energy-dispersive X-ray fluorescence (EDXRF) combined with chemometric strategies. Sixty-four solid mixture samples with variations in the content of sand were evaluated. The normalization strategy to the unit length (‖b‖) in combination with summed spectral data from EDXRF was applied as a strategy to enhance analytical information. The predicted data using a partial least squares (PLS) model were highly correlated with reference values (R2 = 0.9889), and the standard error of cross-validation (SECV) was 2.7 wt% using two latent variables (LV) corresponding to 97% of the explained variance of the data.

The Use of a Micro Near Infrared Portable Instrument to Predict Bioactive Compounds in a Wild Harvested Fruit-Kakadu Plum (Terminalia ferdinandiana).

Kakadu plum (KP; Terminalia ferdinandiana Exell, Combretaceae) is an emergent indigenous fruit originating from Northern Australia, with valuable health and nutritional characteristics and properties (e.g., high levels of vitamin C and ellagic acid). In recent years, the utilization of handheld NIR instruments has allowed for the in situ quantification of a wide range of bioactive compounds in fruit and vegetables. The objective of this study was to evaluate the ability of a handheld NIR spectrophotometer to measure vitamin C and ellagic acid in wild harvested KP fruit samples. Whole and pureed fruit samples were collected from two locations in the Kimberley region (Western Australia, Australia) and were analysed using both reference and NIR methods. The standard error in cross validation (SECV) and the residual predictive deviation (RPD) values were 1.81% dry matter (DM) with an RPD of 2.1, and 3.8 mg g−1 DM with an RPD of 1.9 for the prediction of vitamin C and ellagic acid, respectively, in whole KP fruit. The SECV and RPD values were 1.73% DM with an RPD of 2.2, and 5.6 mg g−1 DM with an RPD of 1.3 for the prediction of vitamin C and ellagic acid, respectively, in powdered KP samples. The results of this study demonstrated the ability of a handheld NIR instrument to predict vitamin C and ellagic acid in whole and pureed KP fruit samples. Although the RPD values obtained were not considered adequate to quantify these bioactive compounds (e.g., analytical quantification), this technique can be used as a rapid tool to screen vitamin C in KP fruit samples for high and low quality vitamin C.

Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

The effect of maturity and tissue on the ability of mid infrared spectroscopy to predict the geographical origin of banana (Musa Cavendish)

SummaryThe objective of this study was to evaluate the effect of sample presentation (tissue type) and maturity (ripe and unripe) on the classification of banana (Musa Cavendish) samples sourced from two different geographical regions and analysed using mid infrared (MIR) spectroscopy. The coefficient of determination (R2) and the standard error of cross‐validation (SECV) obtained using partial least squares discriminant analysis were 0.83 (0.33), 0.75 (0.25) and 0.94 (0.19) for the prediction of maturity, geographical origin and tissue type, respectively. No effect of either of type of tissue (e.g. pulp or peel) or maturity was observed. The results of this study demonstrated that MIR spectroscopy might be used to classify the origin of the banana samples at different degrees of ripeness. However, one of the limitations of this study is on the number of samples analysed and further validation must be recommended using samples from other sources, regions and harvest seasons.

Monitoring two different drying methods of Kakadu plum puree by combining infrared and chemometrics analysis

ABSTRACT The effect of two drying methods (oven and freeze drying) and the addition of maltodextrin to Kakadu plum puree samples (KP) (Terminalia ferdianandiana) were evaluated using mid (MIR) and near-infrared (NIR) spectroscopy. Dry powder samples were obtained using the oven and freeze-drying methods and seven levels of maltodextrin. Training (n = 32) and validation (n = 28) sets were developed for the prediction of moisture (M %), water activity (aw %), hydroxymethylfurfural (HMF) and vitamin C (VITC mg/100 g DM) based on NIR and MIR spectroscopy. Results from this study demonstrated the ability of spectroscopy combined with partial least squares (PLS) regression to monitor these parameters during drying. The standard error in cross validation (SECV) and the residual predictive deviation (RPD) values obtained were of 0.71% (RPD = 4.1) and 0.47% (RPD = 6.1) for M, 0.06% (RPD = 4.4) and 0.02% (RPD = 8.2) for aw, 0.73 (RPD = 3.3) and 0.72 (RPD = 3.3) for HMF, 465.7 mg 100 g DM (RPD = 3.0) and 289.3 mg 100 g DM (RPD = 4.8) for VITC, using MIR and NIR, respectively. The results from this study showed that MIR and NIR spectroscopies are capable of both measuring and monitoring the effect of drying and the addition of maltodextrin as a carrier to KP puree samples.

Detection of Orange Essential Oil, Isopropyl Myristate, and Benzyl Alcohol in Lemon Essential Oil by FTIR Spectroscopy Combined with Chemometrics

Essential oils are high-valued natural extracts that are involved in industries such as food, cosmetics, and pharmaceutics. The lemon essential oil (LEO) has high economic importance in the food and beverage industry because of its health-beneficial characteristics and desired flavor properties. LEO, similar to other natural extracts, is prone to being adulterated through economic motivations. Adulteration causes unfair competition between vendors, disruptions in national economies, and crucial risks for consumers worldwide. There is a need for cost-effective, rapid, reliable, robust, and eco-friendly analytical techniques to detect adulterants in essential oils. The current research developed chemometric models for the quantification of three adulterants (orange essential oil, benzyl alcohol, and isopropyl myristate) in cold-pressed LEOs by using hierarchical cluster analysis (HCA), principal component regression (PCR), and partial least squares regression (PLSR) based on FTIR spectra. The cold-pressed LEO was successfully distinguished from adulterants by robust HCA. PLSR and PCR showed high accuracy with high R2 values (0.99–1) and low standard error of cross-validation (SECV) values (0.58 and 5.21) for cross-validation results of the raw, first derivative, and second derivative FTIR spectra. The findings showed that FTIR spectroscopy combined with multivariate analyses has a considerable capability to detect and quantify adulterants in lemon essential oil.

Effect of sample presentation on the near infrared spectra of wild harvest Kakadu plum fruits (Terminalia ferdinandiana)

Consumer awareness of the health value of native and tropical fruits and its products, is increasing, where demands for new applications are driving food industry to find novel applications of these fruits as functional ingredients. Kakadu plum fruit (Terminalia ferdinandiana) is endemic to Australia and has been used as a functional food or ingredient. This fruit is sold as whole fruit, puree or powder. Critical to the commercialization of this fruit is the development and use of methods and tools to better monitor and understand the quality of these products throughout the value chain. The aim of this study was to evaluate four scanning positions used to collect near infrared (NIR) spectra of Kakadu plum fruits wild harvested, in order to develop a rapid and high throughput method to monitor fruit chemical composition. A portable NIR instrument (Micro-NIR, Viavi, Milpitas, CA, USA) was used to collect the spectra of whole fruit (four positions) samples from the Kimberley region in Western Australia. The NIR data was analysed using principal component analysis (PCA) and partial least squares (PLS) regression models for moisture and total soluble solids. The results showed that the scanning position affected the performance of the models (standard error of cross validation). It was concluded that NIR spectroscopy is a promising method to assure the integrity of the value chain of these products. However, a more robust protocol must be defined when whole fruit are analysed.

Minimal-Invasive Analytical Method and Data Fusion: an Alternative for Determination of Cu, K, Sr, and Zn in Cocoa Beans

In the present study, a minimal-invasive analytical method for determination of Cu, K, Sr, and Zn in cocoa beans was performed using energy X-ray fluorescence (EDXRF) and laser-induced breakdown spectroscopy (LIBS) combined with multivariate calibration. Partial least squares (PLS) chemometric technique was applied to modeling the data, and inductively coupled plasma optical emission spectrometry (ICP OES) technique was the reference method for the chemical elements concentration levels, after microwave acid mineralization. The figures of merit estimated for Cu, Sr, Zn, and K showed good performance, with acceptable trueness values (85–120%). Data fusion strategy between EDXRF and LIBS data was used to enhance the predictive capability of K. In addition, lower standard error of cross-validation (SECV) (872 mg kg−1) was obtained, showing better performance than those obtained by individual data.

Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain

Abstract There has been increased interest in the implementation of near infrared spectroscopy (NIRS) as a non-destructive analytical technique to monitor the quality and safety of vegetables during their growing season and after harvest, throughout the food supply chain. The aim of this work was to evaluate the feasibility of using a portable NIR spectrophotometer (the MicroNIR™ Pro 1700 (spectral range 908–1676 nm) working in reflectance mode) based on Linear Variable Filter (LVF) technology to analyse soluble solid content (SSC) and nitrate content in spinach plants in situ, in the field and along the supply chain. A total of 77 spinach plants were analysed at three control points of the supply chain: 1) in the field, during the growing season and after harvest, 2) in the lab, simulating conditions at receipt at the processing industry and 3) on the leaves in the lab, after washing, thus simulating the analysis of the processed product ready to be packaged, as a previous step for the novel application of NIRS at delivery points and in the supermarkets. The results confirmed the feasibility of using this spectrophotometer throughout the supply chain to establish product quality and safety, which would allow to make real-time decisions related to the agricultural practices, optimum harvest time, industrial uses and commercial shelf-life. The comparison between the models developed for the NIRS analysis in the three control points studied indicated that the recommended procedure would be to take a single spectrum per plant as a suitable way of predicting quality and safety parameters in the field and at the reception points in the industry. Two spectra on each of the two leaves should be taken after the washing operation in the industry, with values of the standard error of cross validation of 1.0 % for SSC and 766 mg kg−1 for nitrate content.

Different supervised and unsupervised classification approaches based on visible/near infrared spectral analysis for discrimination of microbial contaminated lettuce samples: Case study on E. coli ATCC

Escherichia coli is a main cause of microbial contamination in lettuce. Detection of microbial contamination is the essential key to ensure the consumption of fresh lettuce. In this investigation, the potential of Vis/NIR spectroscopy system with chemometrics analysis was probed for detecting different microbial loads (0.1, 0.2 and 0.3 ml concentration of E. coli solution) on the lettuce samples in the wavelength range of 350–1100 nm. Five different chemometrics analyses consist of soft independent modeling of class analogies (SIMCA), support vector machine (SVM), Partial least Squares Discriminant Analysis (PLS-DA) (supervised techniques), principal component analysis (PCA) and hierarchical cluster analysis (HCA) (unsupervised techniques) were used. The results proved that HCA were correctly clustered unsafe samples with 0.2 ml and 0.3 ml microbial contamination. However, some overlapping was observed between safe and unsafe samples with 0.1 ml microbial contamination. Vis/NIR spectral data with pattern recognition methods (SIMCA and SVM) can be obtained acceptable degree of classification (87.1% and 89.39% accuracy, respectively) between safe and unsafe samples. Compare with 5 different methods, the best model was recommended by PLS-DA using standard normal variate (SNV) + second derivate (D2) pre-processing methods in 6 optimal selected wavelengths (520, 670, 700, 750, 900, 970 nm) with the minimum standard error of cross validation (SECV = 0.176). Besides, a good correlation (rc = 0.989) between Vis/NIR spectral data and E. coli contamination proved the possibility of using Vis/NIR spectroscopy system to detection and evaluation of microbial contamination in lettuce.

Prediction of total phenolics, ascorbic acid, antioxidant capacities, and total soluble solids of Capsicum annuum L. (bell pepper) juice by FT-MIR and multivariate analysis

The total phenolic content, antioxidant capacity, ascorbic acid content and total soluble solids content of bell pepper (Capsicum annuum L.) juice were predicted by mid-infrared spectroscopy coupled with chemometrics. The juice of one hundred eighty bell peppers of different colors (green, yellow, orange, and red) was analyzed in terms of spectral and concentration information. The calibration models were obtained by using partial least squares algorithm (PLS1) and considering specific spectral regions for each property. The validation was performed with two approaches, cross-validation (CV) and external-validation (EV). For the PLS1+CV models, 160 samples were used in both the calibration and validation stage. For the PLS1+EV models, 120 samples were used to calibrate and 40 to validate the model. Moreover, additional samples were used for the prediction stage. The determination coefficient (R2), the standard error of calibration (SEC), the standard error of prediction (SEP), the standard error of cross-validation (SECV), and the residual predictive deviation (RPD) were evaluated for each property. The R2 and RPD were 0.934–0.979 and 3.1–5.12, respectively, for the PLS1+CV models, and 0.820–0.931 and 5.20–6.96, respectively, for the PLS1+EV models. The proposed models are a green and straightforward methodology to analyze bell peppers juice properties.

Qualitative and quantitative assessment of cork anomalies using near infrared spectroscopy (NIRS)

Abstract The aim of this study was to develop applications of near infrared spectroscopy (NIRS) to detect certain anomalies in cork used in the manufacture of stoppers for wine bottling. 326 spectra of cork planks were used in a qualitative analysis to evaluate the feasibility of NIRS to discriminate between pure cork, yellow stain and lignified cork. Classification and validation errors varied between 0 % and 11.4 %. In addition, 360 spectra of cork granulate were used in a quantitative analysis to determine the percentage of yellow stain and corkback. A root mean standard error of cross validation (RMSECV) of 1.50 % was obtained for yellow stain percentage (R² = 99.62 %). For corkback percentage RMSECV was 2.96 % (R2 = 98.53 %). These results show that NIRS technology has potential, alongside traditional methods, to determine the quality of cork used to manufacture stoppers.

A Double-Activity (Green Algae Toxicity and Bacterial Genotoxicity) 3D-QSAR Model Based on the Comprehensive Index Method and Its Application in Fluoroquinolones' Modification.

The comparative molecular similarity index analysis (CoMSIA) model of double-activity quinolones targeting green algae toxicity and bacterial genotoxicity (8:2) was constructed in this paper on the basis of the comprehensive index method. The contour maps of the model were analyzed for molecular modifications with high toxicities. In the CoMSIA model, the optimum number of components n was 7, the cross-validated q2 value was 0.58 (>0.5), the standard deviation standard error of estimate (SEE) was 0.02 (<0.95), F was 1265.33, and the non-cross-validated R2 value was 1 (>0.9), indicating that the model had a good fit and predicting ability. The scrambling stability test parameters Q2, cross-validated standard error of prediction (cSDEP), and dq2/dr2yy were 0.54, 0.25, and 0.8 (<1.2), respectively, indicating that the model had good stability. The external verification coefficient r2pred was 0.73 (>0.6), and standard error of prediction (SEP) was 0.17, indicating that the model had a good external prediction ability. The contribution rates of the steric fields, electrostatic fields, hydrophobic fields, hydrogen bond donor, and acceptor fields were 10.9%, 19.8%, 32.7%, 13.8%, and 22.8%, respectively. Large volume groups were selected for modification of ciprofloxacin (CIP), and the derivatives with increased double-activity characterization values were screened; the increase ratio ranged from 12.31–19.09%. The frequency of derivatives were positive and total energy, bioaccumulation, and environmental persistence was reduced, indicating that the CIP derivatives had good environmental stability and friendliness. Predicted values and CoMSIA model constructed of single activities showed that the CoMSIA model of double activities had accuracy and reliability. In addition, the total scores of the derivatives docking with the D1 protein, ferredoxin-NADP (H) reductases (FNRs), and DNA gyrase increased, indicating that derivatives can be toxic to green algae by affecting the photosynthesis of green algae. The mechanism behind the bactericidal effect was also explained from a molecular perspective.

Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples

The present study aims to develop a fast and simple method for the determination of potassium (K), magnesium (Mg) and phosphor (P) in bean seed samples employing a data fusion strategy in the low-level with laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) techniques combined with direct solid sample analysis. Univariate and multivariate (multiple linear regression, MLR) calibration and leave-one-out cross validation strategies were evaluated to build the calibration models correlated with reference values obtained by inductively coupled plasma optical emission spectrometry (ICP OES) after microwave-assisted acid digestion. The proposed calibration models for WDXRF and LIBS were tested using 14 samples, where the best results were obtained using the data fusion of both techniques. The standard error of cross validation (SECV) obtained were: 0.12% for K, 0.019% for Mg and 0.10% for P, and the trueness ranged between 89 and 124% for K, 82 to 125% for Mg and 73 to 128% for P. These values showed a good accuracy, precise and robustness of the method and a greater reliability of the results. In addition, the predicted concentrations ranged from 0.97 to 1.55% for K, 0.14 to 0.28% for Mg, and 0.27 to 0.82% for P.

Broad near-infrared spectroscopy calibrations can predict the nutritional value of &gt;100 forage species within the Australian feedbase

Context Near-infrared reflectance spectroscopy (NIRS) is a tool that permits rapid and inexpensive prediction of the nutritional characteristics of forages consumed by ruminants. Aim Our aim was to investigate the feasibility of developing a NIRS calibration to predict the nutritional value of the majority of grasses, legumes and forbs that are utilised for sheep and cattle production in southern Australia. Methods More than 100 annual and perennial forage species were grown in replicated plots at two locations over a period of 3 years. Biomass was sampled every 3–6 weeks, dried, ground and scanned with a desktop NIRS machine (n = 4385). One-quarter of these samples were subjected to laboratory analysis for calibration development or validation. Key results Despite the large variation in the taxonomy and maturity of the plants when sampled, we successfully developed broad calibrations that predicted key nutritional traits. We achieved excellent predictions for crude protein, with a ratio of standard error of performance:standard deviation (RPD) of 5.3, and standard error of cross validation (SECV) of 1.06%. Predictions of neutral detergent fibre were also excellent (RPD 4.3, SECV. 3.5%). For pepsin–cellulase DM digestibility and acid detergent fibre, predictions were very good (RPD 3.7, SECV 2.6% and RPD 3.9, SECV 2.1%). Predictions for organic matter were less reliable (RPD 2.2). We achieved very promising predictions of methane production during batch culture fermentation (RPD 3.1, SECV 3.5 mL/gDM). Predictions of ammonia and total volatile fatty acid concentrations in the post-fermentation substrate were poor. Conclusions We found that the broad calibrations predicted the nutritional traits of annual grasses, annual legumes and forb species with greater accuracy than perennial grasses or legumes. This could be associated with the accuracy of the wet chemistry methods. As a general rule, separating taxonomically similar species into groups before the development of calibrations, did not lead to more accurate predictions. Implications If more spatial and temporal diversity can be built in without a large reduction in accuracy, these broad NIRS calibrations represent a valuable tool for Australian researchers, feed testing agents and livestock producers, as they encompass nearly all of the species that appear in monocultures or mixed swards.

The application of near infrared spectroscopy to predict faecal nitrogen and phosphorus in multiple ruminant herbivore species

Near infrared spectroscopy (NIRS) was applied to determine faecal nitrogen and phosphorus using a temporo-spatially diverse dataset derived from multiple ruminant herbivore species (i.e. cattle, bison, deer, elk, goats, and sheep). Single-species NIRS calibrations have previously been developed to predict faecal constituents. Multi-species NIRS calibrations have previously been developed for herbivore faecal nitrogen but not for faecal phosphorus. Faecal samples representing a herd or flock composite were analysed via NIRS (400–2498nm). Calibration sets for faecal nitrogen and phosphorus were developed from: (1) all samples from all six species, (2) all cattle samples only, (3) all samples except those from bison, (4) all samples except those from deer, (5) all samples except those from elk, (6) all samples except those from goats, and (7) all samples except those from sheep. Validation sample sets included: (1) each of the individual species (predicted with a cattle only-derived calibration), and (2) each of the individual species (other than cattle) predicted with a multi-species calibration constructed from all cattle samples plus those samples from the remaining four species (i.e. ‘leave-one-out’). All multiple coefficient of determination (R2) values for faecal nitrogen calibrations were ≥0.97. Corresponding standard error of cross validation (SECV) values were ≤0.13. Validation simple coefficient of determination (r2) and standard error of prediction (SEP) of each alternate species using the cattle-derived calibration ranged from 0.76 to 0.84, and 0.28 to 0.5 respectively. Similar values for the sequential species leave-one-out validation for faecal nitrogen were 0.67 to 0.89, and 0.17 to 0.47 respectively. All R2 values for faecal phosphorus calibrations were ≥0.79; corresponding SECV were ≤0.14. Validation r2 and SEP of each alternate species using the cattle-derived phosphorus calibration were ≤0.63 and ≥0.13 respectively. Similar values for the sequential species leave-one-out validation were ≤0.66 and ≥0.22 respectively for faecal phosphorus. Multi-species faecal NIRS calibrations can be developed for monitoring applications in which determination of faecal nitrogen is appropriate, e.g. free-ranging herbivore nutrition, nitrogen deposition from animal faeces on rangelands with declining forage quality, or runoff from confined animal feeding operations. Similar calibrations for faecal phosphorus require additional research to ascertain their applicability.

Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections

AbstractBrachiaria humidicola (BH) (syn. Urochloa humidicola) is an important forage grass in the tropics due to its capacity to grow in nutrient‐deficient soils, tolerate waterlogging, and inhibit soil nitrification. A major objective of BH breeding is to improve its nutritional quality. Therefore, a rapid and low‐cost method is needed to assess main quality parameters such as neutral detergent fiber (NDF), acid detergent fiber (ADF), in vitro dry matter digestibility (IVDMD), and crude protein (CP). This study developed models using near infrared reflectance spectroscopy (NIRS) to predict concentrations of these parameters toward breeding. Samples were collected from BH trials located in different regions of Colombia, scanned for NIRS (400–2,500 nm), analyzed with wet chemistry as reference values, and used to build the chemometric models. Results from wet chemistry showed wide variability in terms of dry matter percentage for NDF (51.6–76.2%), ADF (26.1–46.1%), IVDMD (41.5–78.3%), and CP (2.8–12.8%). The NIRS models were validated using an independent set of samples and have coefficients of determination (R2) and one minus the variance ratio (1 – VR) values in the range of .9 and .95, suggesting a good correlation between reference‐lab and NIRS‐predicted values. The standard errors of cross validation (SECV) for IVDM, NDF, ADF, and CP were 1.59, 1.18, 0.74, and 0.53%, respectively. Prediction efficiency (ratio of performance to standard deviation, RPD) for all parameters was above 3.0, except for CP (2.6). Calibrations obtained present an adequate adjustment and predictive tendency, making them suitable for selection and BH breeding.

PH Prediction of Perlis Sunshine Mango Using NIR Spectrometer

To evaluate the various internal quality attributes of fruits, NIR spectroscopy techniques are undoubtedly quick and non-destructive tools. Acidity (pH) is one of the main quality attributes in mango fruits. Generally, it is being predicted in destructive way. The aim of this research was to develop calibration model and prediction of pH in Perlis Sunshine mangoes using NIR spectrometer. The transmission spectra of Sunshine mangoes were acquired in the wavelength range from 300 to 1000 nm. The effects of different types of pre-processing methods and spectra treatments, such as baseline correction, multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, second order derivative (SG) and normalisation were analyzed. The prediction models were developed by partial least squares (PLS) regression. The coefficient of determination (R2) of pH was 0.928 and the standard error of cross-validation (SECV) was 0.153. The results indicated that by using the NIR measurement system, in the suitable spectral range, it is possible to predict the pH of mango fruits by non-destructively.

An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling

This study established an approach to rapidly determine the Tween-80 in traditional Chinese medicine (TCM) injection by using near-infrared (NIR) spectroscopy. Totally 133 standard solutions of Tween-80 were prepared and randomly divided into calibration set and validation set, containing 109 and 24 samples, respectively. Spectral data were preprocessed and then subjected to establish a predictive model using partial least-squares (PLS). The standard error of cross validation (SECV), standard deviation of calibration (SEC), and the determination coefficient (R) of the established model were 0.0561, 0.0526, and 0.9986, respectively. The model was successfully applied to determine Tween-80 contents in 25XBJ Injectionsand 40FFSX Injections, and it produced satisfactory quantitative analysis results with average relative deviations 0.60% and 0.16%, respectively, for 25XBJ Injectionsand 40FFSX Injections, and the maximum relative deviations 8.57% and 7.60%, respectively. This work shows that NIR model displayed quite good predictive ability for Tween-80 quantitative analysis, which could potentially be applied to rapid determination of Tween-80 in the production process of TCM injections and other TCM products.