Near-infrared Spectroscopy Method Research Articles

Species-Specific quantification of bioactive boswellic acids in Boswellia resin using NIR spectroscopy, HPLC and Multivariate data analysis

The bioactive compounds Acetyl-11-keto-β-boswellic acid (AKBA) and 11-keto-β-boswellic acid (KBA), found in the resin of the Boswellia tree, exhibit anti-inflammatory properties, rendering Boswellia resin an intriguing natural medicinal products. However, the content of boswellic acids varies across different Boswellia species and proper knowledge of its species-dependent nature, as well as alternatives to the resource- and time-intensive HPLC analysis, are lacking. Here we present a comprehensive investigation into the boswellic acid content of seven Boswellia species from ten countries and introduce a novel and non-destructive Near-Infrared spectroscopy method for predicting boswellic acid concentrations in solid resin samples. The HPLC-UV reference analysis revealed AKBA concentrations of up to 7.27 % (w/w) with KBA concentrations reaching up to 1.28 % (w/w). Principal Component Analysis of the HPLC and NIR spectroscopy data unveiled species-specific variations, facilitating differentiation based on boswellic acid content, characteristic chromatograms and NIR spectra. Using the HPLC-UV quantification as reference, we developed a Partial Least Squares regression model based on NIR spectra of the resin samples. This model demonstrated highly satisfactory predictive capabilities for AKBA content, achieving a root mean square error of prediction of 0.74 % (w/w) and an R2val of 0.79 in independent test set validation. Although the model was less effective for predicting KBA content, it still offered valuable estimates. The spectroscopic method introduced in this study provides a cost-effective and solvent-free approach for predicting boswellic acid content, demonstrating the potential for application in non-laboratory settings through the use of miniaturized NIR spectrometers. Consequently, this method aligns well with the principles of green chemistry and addresses the growing demand for alternative analytical techniques.

Prediction of Density in Standing Trees of Various Wood Species in Natural Forests Using Near-infrared Spectroscopy

Density plays an important role as basic information for applying wood as construction materials. Recent years, the application of near-infrared spectroscopy as non-destructive testing (NDT) has been promising. Density prediction for standing trees in huge variation trees and species of natural forest needs to be investigated using NDT as of eco-green harvesting. The combination of density information and near-infrared spectroscopy is enabled to build a prediction model. This research applied increment cores sampling for density prediction analysis using near-infrared spectroscopy method. The research combined increment cores samples from multiple wood species to be analyzed in one chemometrics analysis of cross-validation partial least squares regression (CV-PLSR) to build a prediction model of density. The research resulted coefficient of determination for cross-validation (R2CV) of 0.76 with number of latent variable (LV) 10 from the 1st derivative with 13 smoothing-point spectra and wavelength of 1200 – 1800 nm as the best prediction model. The result seemed sufficient enough with those number of LV for this small tube wood sampling of increment cores from multiple wood species. This research proved that building a prediction model for multiple wood species is possible to be done.

Non-Destructive Prediction of Chemical Content in Palm Oil Fruit Using Near-Infrared Spectroscopy and Artificial Neural Network

Oil and water content are an important quality criteria of crude palm oil (CPO) resulted from palm oil fruit processing. Those contents are usually determined using chemical method in the laboratory. This method is time consuming, long procedure, and destructive. Some efforts had been carried out to determine oil and water content of palm oil fruit non-destructively using some methods including Near-Infrared Spectroscopy (NIRS), but the results had not been satisfied. This research aims to assess Artificial Neural Network (ANN) and NIRS method to predict oil and water content of palm oil fruit’s non-destructively. The samples were palm oil fruits with ten maturity levels harvested from plantation in Bogor. Sample’s reflectance was measured with spectrometer NIR-Flex 500 at wavelength of 1000-2500 nm. After that, oil and water content were determined using chemical method. Some pre-treatments of NIR spectra namely normalization, savitzky-golay first derivative, their combinations, and standard normal variate were applied. Multivariate analysis such as PLS were carried out and the results of Factor Component (FC) were input for ANN model. The result showed the best method to predict oil content was combination savitzky-golay first derivative and normalization pre-treatment using PLS-ANN with 20 FC (R2=0.99; SEC=0,58%, RPD = 29.89; CV = 2.47%). For water content, the best prediction was standard normal variate pre-treatment using PLS-ANN with 20 FC (R2=0.99; SEC=1,07%, RPD=20.68; CV=1,73%). The result shows that developed ANN and NIRS can predict oil and water content of palm oil fruit non-destructively.

Assessment of Persian export pomegranate quality: A reliable non-destructive method based on spectroscopy and chemometrics

The present study investigated the feasibility of visible near-infrared (Vis-NIR) spectroscopy method as a low-cost, quick and reliable method for the prediction of qualitative characteristics (fruit juice percentage, soluble solids, acidity, taste index and vitamin C) of an export variety of Persian pomegranate. For this purpose, pomegranate samples were first analyzed by Vis-NIR spectroscopy, followed by destructive reference methods for quality determination. Subsequently, the data from Vis-NIR were evaluated with different pre-processing methods, among which the combination of the Savitzky–Golay (SG) smoothing model and standard normal variation (SNV) normalization model, generates the best outcomes in predicting six desired qualitative characteristics by partial least square regression (PLSR) model. Fruit juice percentage and pH presented the best prediction models with a correlation coefficient (rP) of 0.98 for both, and root mean square errors of prediction (RMSEP) equal to 0.036 and 0.041, respectively. The values of rp for all desired parameters in this study were higher than 0.95, which pointed to the excellent accuracy of the PLSR models in the prediction of pomegranate qualitative features. It can be concluded from the present study that, Vis-NIR spectroscopy using the PLSR model as a non-destructive functional approach is capable of verifying the quality of Persian export variety pomegranate as an online technique with the aim of grading products before the export phase in order to augment the product marketability.

Non-Contact Measurement of Alcohol Content Based on Near Infrared Spectroscopy

In order to solve the influence of other external factors on the measurement concentration of alcohol solution, a non-contact measurement method based on near-infrared spectroscopy and BP neural network temperature correction method was proposed in this paper. This method takes into account the influence of temperature on the light intensity of alcohol liquid solution in the detection process, and trains it together with the measurement data as the input of neural network. At the same time, it is compared with the index fitting algorithm, which proves that the method has a good effect on the non-contact measurement of alcohol content.

Identification and quantification of adulteration in frozen-thawed meat of different breeds by NIR spectroscopy and chemometrics

This work aims to compare the characteristic near-infrared absorption bands of different breeds of meats and to develop a qualitative and semi-quantitative method for the detection of adulteration in various types of frozen meat based on near-infrared spectroscopy and chemometrics methods. The adulterated samples were in various combinations of pork, beef and mutton adulterated with different proportions of pork, beef, lamb, duck and chicken. The results showed that the accuracy of the PLS-DA model that identified all adulteration types together was the lowest (AUCP= 0.8265), while the accuracy of the PLS-DA model of lamb-chicken and beef-chicken was the highest (AUCP= 1). AUCP of other adulteration types was higher than 0.92. And the PLSR model constructed by all the adulteration types together had the worst result (Rp2= 0.5348, RMSECV=0.1895, RPD=1.48). The results of PLSR prediction models constructed by the adulteration types separately all reached the expectation (Rp2= 0.8707–0.9703，RMSECV= 0.0637–0.1089，RPD= 2.78–5.80). The results of the PLS-DA models and PLSR models using characteristic band spectral information are basically the same or slightly improved compared with those results of the models using full-band spectral information. Therefore, NIR spectroscopy and chemometrics methods can directly qualitatively identified all adulteration types, but it is more accurate to semi-quantitatively predict the proportion of adulteration according to different adulteration types. The near-infrared absorption of moisture and fat contributed more to the detection of meat adulteration.

Near-Infrared Spectroscopy During Reactive Hyperemia for the Assessment of Lower Limb Vascular Function.

Vascular diseases of the lower limb contribute substantially to theglobal burden of cardiovascular disease and comorbidities such as diabetes. Importantly, microvascular dysfunction can occur prior to, or alongside, macrovascular pathology, and both potentially contribute to patient symptoms and disease burden. Here, we describe a non-invasive approach using near-infrared spectroscopy (NIRS) during reactive hyperemia, which provides a standardized assessment of lower limb vascular (dys)function and a potential method to evaluate the efficacy of therapeutic interventions. Unlike alternative methods, such as contrast-enhanced ultrasound, this approach does not require venous access or sophisticated image analysis, and it is inexpensive and less operator-dependent. This description of the NIRS method includes representative results and standard terminology alongside the discussion of measurement considerations, limitations, and alternative methods. Future application of this work will improve standardization of vascular research design, data collection procedures, and harmonized reporting, thereby enhancing translational research outcomes in the areas of lower limb vascular (dys)function, disease, and treatment.

Spatial sensitivity to absorption changes for various near-infrared spectroscopy methods: A compendium review

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media, particularly for measurements relevant to near-infrared spectroscopy. The three temporal domains, continuous wave, frequency domain, and time domain, each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient. Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change. Therefore, spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains. The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media. These works are experimental and theoretical, presenting one-, two-, and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods, domains, and data types. Following this history, we present a compendium of sensitivity maps organized by temporal domain and then data type. This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document. Methods for one to generate these maps are provided in Appendix A , including the code. This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize, investigate, compare, and generate sensitivity to localized absorption change maps.

Mid-infrared-near-infrared double-resonance spectroscopy of molecules with kilohertz accuracy.

Precision measurements of molecular transitions to highly excited states are needed in potential energy surface modeling, state-resolved chemical dynamics studies, and astrophysical spectra analysis. Selective pumping and probing of molecules are often challenging due to the high state density and weak transition moments. We present a mid-infrared and near-infrared double-resonance spectroscopy method for precision measurements. As a demonstration, Doppler-free stepwise two-photon absorption spectra of 13CO2 were recorded by pumping the fundamental transition of R14 (00011)-(00001) and probing the P15 (00041)-(00011) transition enhanced by a high-finesse optical cavity, and the transition frequencies were determined with an accuracy of a few kilohertz.

An attempt to identify milk protein fraction genotypes using unsupervised and supervised near-infrared spectroscopy methods

The aim was to evaluate near-infrared spectroscopy (NIRS) potential to discriminate among β-casein (CN), κ-CN and β-lactoglobulin (LG) genotypes to be used as an authentication method. A total of 168 milk samples with known genetic information for β-CN, κ-CN and β-LG were collected at the same farm and paired with the NIRS spectrum. Spectra were evaluated with an unsupervised method (principal component analysis, PCA) and a supervised method (partial least squares-discriminant analysis, PLS-DA). For the PLS-DA, data were split into a train (75%) and a test set (25%), and the variable in projection >1 criterion was applied to select informative wavelengths. Results obtained confirmed that milk quality was similar among genetic variants. For the PCA, the observed variance explained by the first two principal components was 94%, but samples were not clustered by their genotypes of β-CN (i.e. A1A2, A2A2), κ-CN (i.e. AA, AB, AE, BB, BE) and β-LG (i.e. AA, AB, BB). The best accuracy for the PLS-DA models was reached by β-CN (train and test set, 64%), followed by β-LG (train set, 56%; test set, 52%) and κ-CN (train set, 41%; test set, 36%). In conclusion, the PCA on milk spectra was not able to cluster β-CN, κ-CN and β-LG genotypes, but the PLS-DA models revealed promising results for β-CN and β-LG. It could be interesting to increase the number of samples to equilibrate genetic variants and to apply a sampling selection method before discarding the applicability of NIRS as an authentication method.

Evaluation of cerebral autoregulation of oxygen by NIRS method during postnatal transition period in term and late preterm newborns without resuscitation requirement.

Pulse oximetry is commonly used to monitor arterial oxygen saturation and heart rate during the transition period and reference intervals have been determined. However, the effect of the change in arterial oxygen saturation on tissue oxygenation does not seem to be the same. So, a non-invasive method for monitoring cerebral or regional tissue oxygenation will be potentially useful for vulnerable infants. This study aims to evaluate the effectiveness of cerebral autoregulation in the first 10 min after delivery in term and late preterm newborns without resuscitation requirement. Cerebral tissue oxygen saturation was measured in the first 10 min after birth with near-infrared spectroscopy (NIRS) from the left forehead. Peripheral oxygen saturation was measured with pulse oximetry from the right hand and cerebral fractional tissue oxygen extraction was calculated. Nineteen late preterms and 20 term infants were included in the study. There was no statistically significant difference between median cerebral tissue oxygen saturation and cerebral fractional tissue oxygen extraction values of late preterm and term infants (p < 0.001). There was a strong inverse relationship between cerebral tissue oxygen saturation and cerebral fractional tissue oxygen extraction (p < 0.001). In late preterm infants similar to term infants, arterial oxygen saturation and cerebral tissue oxygen saturation increased with time, but inverse reduction of cerebral fractional tissue oxygen extraction showed the presence of an active autoregulation in the brain. This can be interpreted as the ability of the brain to protect itself from hypoxia by regulating oxygen uptake during normal fetal-neonatal transition process. A larger scale multi-center randomized control trial is now needed to further inform practice.

VIS-NIR spectroscopy and environmental factors coupled with PLSR models to predict soil organic carbon and nitrogen

Soil profile organic carbon (OC) and total nitrogen (TN) are influenced by topographic attributes, and land use. The visible and near-infrared (Vis-NIR) spectroscopy method can be used for the prediction of OC and TN because it is reliable, nondestructive, fast, and cost-effective. VIS-NIR soil spectral and environmental data were combined with the Partial least squares regression (PLSR) model to examine the effect of topography attributes and land use on topsoil and subsoil OC and TN stocks. After this, based on the soil depth, 114 soil samples were collected from 0 to 20 cm (topsoil) and 20–50 cm (subsoil) under three land uses, as well as OC and TN, along with several soil properties including soil particles (sand, silt, clay), pH, and bulk density in both topsoil and subsoil samples were measured. A DEM with a resolution of 30 m was used to derive the topography factors and remote sensing data was used to calculate the vegetation index. Soils (0–50 cm) under orchard land use had the highest stock of SOC (7.4 kg m−2) as well as TN (2.4 kg m−2). There was a significant increase in the organic matter stock of soils located on the south aspect (8.3 kg m−2) compared to soils located on other aspects, particularly on the north aspect (3.9% increase). Soils on the south aspect contain higher soil-water contents and lower temperatures, resulting in a decrease in the decomposition of soil organic matter. A strong positive correlation was demonstrated between topography wetness index (0.57–0.63) and topography TN stocks (0.54–0.66) as well as the highest loading score among terrain attributes, suggesting that topography is the primary factor controlling SOC stocks, particularly subsoil stocks. Additionally, we found that soils on the south-facing aspects (N aspects) had the highest spectra. Additionally, the PLSR, which showed an R2 of 0.82, a RMSE of 0.15 %, and a RPD of 0.39 indicated excellent prediction capabilities for the OC content. We concluded that the PLSR model coupled with Vis-NIR spectroscopy is able to predict topsoil and subsoil OC and N content under different aspect slopes.

Using near-infrared spectroscopy to predict nitrogen and phosphorus concentrations of herbarium specimens under different storage conditions

BackgroundHerbaria are becoming increasingly important as archives of biodiversity, and play a central role in taxonomic and biogeographic studies. There is also an ongoing interest in functional traits and the way they mediate interactions between a plant species and its environment. Herbarium specimens allow tracking trait values over time, and thus, capturing consequences of anthropogenic activities such as eutrophication. Here, we present an open, reproducible, non-destructive workflow to collect leaf trait data from herbarium specimens using near-infrared spectroscopy (NIRS), and a proof of concept for the reliability of this approach.ResultsWe carried out three experiments to test the suitability of non-destructive NIRS methods to predict leaf traits both for fresh and dried leaves: (1) With a fertilization experiment, we studied whether NIRS was able to capture changes in leaf N and leaf P during a fertilization experiment and we compared contents predicted by NIRS with results obtained from regular wet lab methods. Calibration models for leaf nitrogen and phosphorus contents had a quality of R2 = 0.7 and 0.5, respectively. We fitted calibration models for NIRS readings on fresh and dried leaf samples, both of which produced equally precise predictions compared to results from wet lab analyses. (2) We tested the effect of herbarium conservation on NIRS readings by simulating them through the application of six treatments combining freezing, drying and pesticide spraying in a factorial scheme and comparing these with untreated samples. No consistent changes were observed in the spectra quality before and after the simulated herbarium conditions. (3) Finally, we studied the effect of specimen storage duration using specimens from a 2018 study which were re-analyzed and compared with spectra obtained in 2021. No consistent changes in spectra were observed after the storage period.ConclusionsThe results demonstrate the reliability of NIRS to measure leaf N and P on herbarium samples. Together with the calibration method and dataset presented here, they provide a toolset allowing researchers to study the development of leaf traits and their response to environmental changes over decades and even centuries in a fast and non-destructive manner.

Application of near infrared spectroscopy for identification and quantitative determination of amino acids in their crystalline and salt forms in the preparation of animal feed

Methods. For the first time, a comprehensive calibration model has been developed and presented for the rapid determination of basic near-infrared spectroscopy (IR) methods in feed amino acids, with application in the production of animal feed. The research principle is based on the Fourier equation for spectroscopy. In this work, Fourier methods in the Italian region (FTIR, FT-NIRS) were applied. The data obtained from the calibration models were confirmed using high-performance liquid chromatography. FT-NIR predictions agreed well with the chromatography data and had predictive deviation (RPD) values &gt;1.3 in all cases.Results. The results indicate that FT-NIR spectroscopy can be used as a simple and rapid tool for monitoring amino acids. In the course of the work, experimental confirmation of previously known facts was obtained — the possibility of visual separation of the spectra of not only counterfeit amino acids, but also the possibility of separation by L- and DL-optical isomers. The work shows that the discrepancies between the values obtained by the classical “wet chemistry” method and the values obtained from the constructed calibration models do not exceed the reproducibility limits of arbitration methods. A predictive model was used based on information flow elements using the OPUS/QUANT2 software package for multivariate calibration and construction of calibration models for amino acids. This chemometric analysis proved the fundamental possibility of determining amino acids in the product. It has been shown that the use of information channels opens up opportunities for the use of many chemometrics algorithms, including data preprocessing and the construction of predictive models.

Contraction intensity affects NIRS-derived skeletal muscle oxidative capacity but not its relationships to mitochondrial protein content or aerobic fitness.

To further refine the near-infrared spectroscopy (NIRS)-derived measure of skeletal muscle oxidative capacity in humans, we sought to determine whether the exercise stimulus intensity affected the τ value and/or influenced the magnitude of correlations with in vitro measures of mitochondrial content and in vivo indices of exercise performance. Males (n = 12) and females (n = 12), matched for maximal aerobic fitness per fat-free mass, completed NIRS-derived skeletal muscle oxidative capacity tests for the vastus lateralis following repeated contractions at 40% (τ40) and 100% (τ100) of maximum voluntary contraction, underwent a skeletal muscle biopsy of the same muscle, and performed multiple intermittent isometric knee extension tests to task failure to establish critical torque (CT). The value of τ100 (34.4 ± 7.0 s) was greater than τ40 (24.2 ± 6.9 s, P < 0.001), but the values were correlated (r = 0.688; P < 0.001). The values of τ40 (r = -0.692, P < 0.001) and τ100 (r = -0.488, P = 0.016) correlated with myosin heavy chain I percentage and several markers of mitochondrial content, including COX II protein content in whole muscle (τ40: r = -0.547, P = 0.006; τ100: r = -0.466, P = 0.022), type I pooled fibers (τ40: r = -0.547, P = 0.006; τ100: r = -0.547, P = 0.006), and type II pooled fibers (τ40: r = -0.516, P = 0.009; τ100: r = -0.635, P = 0.001). The value of τ40 (r = -0.702, P < 0.001), but not τ100 (r = -0.378, P = 0.083) correlated with critical torque (CT); however, neither value correlated with W' (τ40: r = 0.071, P = 0.753; τ100: r = 0.054, P = 0.812). Overall, the NIRS method of assessing skeletal muscle oxidative capacity is sensitive to the intensity of skeletal muscle contraction but maintains relationships to whole body fitness, isolated limb critical intensity, and mitochondrial content regardless of intensity.NEW & NOTEWORTHY Skeletal muscle oxidative capacity measured using near-infrared spectroscopy (NIRS) was lower following high-intensity compared with low-intensity isometric knee extension contractions. At both intensities, skeletal muscle oxidative capacity was correlated with protein markers of mitochondrial content (in whole muscle and pooled type I and type II muscle fibers) and critical torque. These findings highlight the importance of standardizing contraction intensity while using the NIRS method with isometric contractions and further demonstrate its validity.

A comprehensive “quality-quantity-activity” approach based on portable near-infrared spectrometer and membership function analysis to systematically evaluate spice quality: Cinnamomum cassia as an example

Spices have long been popular worldwide. Besides serving as aromatic and flavorful food and cooking ingredients, many spices exhibit notable bioactivity. Quality evaluation methods are essential for ensuring the quality and flavor of spices. However, existing methods typically focus on the content of particular components or certain aspects of bioactivity. For a systematic evaluation of spice quality, we herein propose a comprehensive “quality-quantity-activity” approach based on portable near-infrared spectrometer and membership function analysis. Cinnamomum cassia was used as a representative example to illustrate this approach. Near-infrared spectroscopy and chemometric methods were combined to predict the geographical origin, cinnamaldehyde content, ash content, antioxidant activity, and integrated membership function value. All the optimal prediction models displayed good predictive ability (correlation coefficient of prediction > 0.9, residual predictive deviation > 2.1). The proposed approach can provide a valuable reference for the rapid and comprehensive quality evaluation of spices.

Prediction of Caffeine and Protein of Arabica Coffee Beans Using Near Infrared Spectroscopy (NIRS)

Testing the chemical components of coffee beans has so far been carried out conventionally with laboratory analysis which requires a long time and is expensive. Technological advances allow testing of chemical components to be carried out quickly and accurately using the NIRS (Near Infra Red Spectroscopy) method. This research aims to develop a prediction model for caffeine and protein in Solok Radjo coffee beans using the NIRS method. Solok Radjo coffee is a type of Arabica with specialty grade because of its very strong character, aroma and taste. A total of 30 samples with a weight of 6 g per sample were used in this study. This research uses NIRS Type FT-IR IPTEK T-1516 with a wavelength of 1000 - 2500 nm. The partial least squares (PLS) method was used to process the data with several SNV, MN, and MSC pretreatments to improve the model. The research results show that caffeine is found at wavelengths of 1456 - 1475 nm, 1937 - 1974 nm. Proteins 1455 - 1475 nm, and 1935 - 1974 nm. MSC pretreatment is able to improve PLS performance results. Caffeine calibration values are R2 = 0.996 and SEC = 0.002%, validation values R2 = 0.989, SEP = 0.002%, and RPD 11.869 while protein calibration R2 = 0.999 and SEC = 0.004%, Validation values R2 = 0.999, SEP = 0.010%, and RPD 19,943. NIRS can be used to predict the chemical components of Solok Radjo coffee non-destructively using the PLS method. Key work: Caffeine, NIRS, PLS, predict, protein

APPLICATION OF NEAR INFRARED SPECTROSCOPY (NIRS) IN THE MEASUREMENT OF OIL LOSS AND ITS ACCURACY COMPARED TO SOXHLET METHOD

Loss of oil during crude palm oil (CPO) extraction occurs among other things, due to the use of high temperature and pressure in the boiling process so that the oil from the fruit is absorbed and remains in the empty fruit bunches (EFB). This oil loss is generally measured using Soxhlet method. However, recently in several palm oil mills (POM), the Soxhlet method has been replaced by the Near Infrared Spectroscopy (NIRS) method which is equipped with free and open-source software (FOSS). Therefore, the purpose of this study was to examine the application of NIRS in measuring oil loss in EFB and to test its accuracy compared to the Soxhlet method. The data is from primary source of POM during April-June 2022 period (peak harvest times). Normality test and paired T test is used to analyze data. This research also cites case studies of CPO processing companies in Indonesia in the last 10 years (2013-2022) with a focus on oil loss in EFB. The results showed that the oil loss in EFB using NIRS and Soxhlet were 0.50% and 0.49%, respectively. These two values are below the company's oil loss standard (0.62%). Both data are normally distributed and based on the paired T test, it was found that there were no significant differences in the results of oil loss analysis using the NIRS and Soxhlet methods. Compared with data for the last 10 years, they are also below the standard limit of other companies which reaches 3%. Therefore, NIRS is more recommended in terms of time efficiency, safe and does not damage the material.

Non-destructive Identification of the geographical origin of red jujube by near-infrared spectroscopy and fuzzy clustering methods

ABSTRACT The red jujube quality is closely associated with its place of origin. In order to quickly and easily identify the geographical origin of red jujube, the classification of red jujube samples’ near-infrared reflectance (NIR) spectra was performed using several fuzzy clustering methods in combination with principal component analysis (PCA) and linear discriminant analysis (LDA). Firstly, a NIR-M-R2 portable near-infrared spectrometer was used to collect four varieties of red jujube samples from four representative producing areas in four provinces: Gansu, Henan, Shanxi and Xinjiang in China. Each variety corresponded to a producing area, and it had 60 samples with a total of 240 samples. Near-infrared spectra of red jujube were acquired using a NIR-M-R2 portable near-infrared spectrometer, and the initial near-infrared spectra were preprocessed by Savitzky-Golay (SG) filtering. Secondly, PCA and LDA were used to further process the NIR data for dimension reduction and feature extraction, respectively. Finally, red jujube samples were classified by fuzzy C-means (FCM) clustering, Gustafson-Kessel (GK) clustering and possibility fuzzy C-means (PFCM) clustering. When GK served as the clustering algorithm, the clustering accuracy was the highest, as the value of 98.8%. Based on the experimental results, it was evident that the GK clustering algorithm played a significant role in identifying the place of origin of red jujube with near-infrared spectroscopy.

Development of comprehensive calibration models for pulp traits by near infrared spectroscopy (NIRS) for pure species and hybrids of Corymbia and Eucalyptus

Corymbia hybrid has emerged as a complimentary pulpwood in India along with Eucalyptus camaldulensis and eucalypt hybrids for enhancing the profitability of the industry and the income of farmers. Near infrared spectroscopy (NIRS) offers a rapid non-destructive method for estimating wood and pulp traits of short rotation trees. Development of NIR calibration models in Corymbia is essential for quick selection and rapid advancement of pulp wood traits. A study was carried out to develop a combined calibration curve for pure E. camaldulensis and Corymbia species and their interspecific hybrids. Wood samples were extracted from four-year-old 300 trees of Corymbia torelliana (CT), C. citriodora (CC), C. variegata (CV) and E. camaldulensis (EC) and their interspecific hybrids from progeny evaluation trials at three different locations in south India. The wood logs and powdered core samples were analyzed for kraft pulp yield (KPY), lignin and S|G ratio by wet chemistry and NIRS method. The KPY values of CC and CV ranged from 44 to 52%, whereas CT, a maternal parent in CT x CC|CV hybrids, had a narrow range of 38-46.5%. However, the interspecific hybrids of CT x CC|CV exhibited a range with higher values from 48.5 to 50.5%, indicating a narrow scope for predicting hybrids from that of parent’s standard calibration curves. The estimated values in pure species of Corymbia for lignin content ranged from 22.7 to 29.2 % and 1.9 to 3.1 for S|G ratio, while in hybrids it was 25.6 to 29.4 for lignin. The correlation coefficients for cross-validation of KPY in CC, CV, CT and their hybrids were 0.8, 0.81, 0.89 and 0.72, respectively, and root mean square errors (RMS) were 0.8, 0.55, 0.73 and 0.31, respectively. When CC and CV samples were pooled to generate a common prediction model, the correlation coefficients were 0.8 for KPY, 0.75 for lignin and 0.83 for S|G ratio, with RMS of 0.68 for both KPY and lignin, and 0.12 for S|G ratio. Corymbia hybrids were evaluated with 99% accuracy using a combined prediction model of pure Corymbia species and hybrids. The method was validated with 80 samples of pure species and hybrids with a RMS of less than 1.0. We also attempted in developing a combined prediction model for different species of Corymbia, Eucalyptus, and their hybrids with minimum prediction errors.