Near-infrared Analysis Research Articles

IDENTIFICATION OF LIGNOCELLULOSE-LIKE MATERIAL USING SPECTROSCOPY ANALYSIS

Lignocellulose materials, such as bamboo, rattan, and wood, have been largely used for furniture and crafts. On the other hand, the utilization of lignocellulose-like materials, which have a similar texture and appearance to those from nature, has been increasing recently due to their superior durability. This research aimed to identify the lignocellulose-like material using spectroscopy analysis, such as Raman and Near Infrared (NIR) which is well-known as a non-destructive, quick, and accurate approach for material identification. We investigated 4 types of lignocellulose-like materials that were provided by Dewan Serat Indonesia (The Indonesian Fiber Council) from an industry that produces them. The NIR analysis was performed at wavenumbers 10,000-4,000 cm-1. The natural lignocellulose (bamboo and wood) and the polymers (polyethylene and polyproline) were used as standards. Raman analysis was further employed to identify the composition of selected lignocellulose-like materials by comparing their spectra with the library software. The results showed that the original NIR spectra of lignocellulose-like and those natural materials were different, indicating that the NIR analysis can differentiate those materials. The NIR spectra of lignocellulose-like materials were similar to those of polyethylene spectra. Those lignocellulose-like were also identified as polyethylene due to the similarity of the Raman spectra and their library spectra.

Intelligent monitoring of post-processing characteristics in 3D-printed food products: A focus on fermentation process of starch-gluten mixture using NIR and multivariate analysis

The production of three-dimensional (3D)-printed food products requires not only optimal 3D-printing adaptability but also appropriate post-processing characteristics. This study aimed to use near infrared (NIR) spectroscopy to predict the rheological properties of 3D-printed dough, enabling intelligent monitoring of the dough's fermentation process. Utilizing support vector machine (SVM) classification model, the fermentation stages can be classified as under-fermentation, complete fermentation, and over-fermentation. Employing preprocessing methods with Synergy Interval Partial Least Square-Competitive Adaptive Reweighted Sampling (SIPLS-CARS) algorithm, 27, 39, 23, and 27 key wavelengths were filtered from the raw NIR spectral data, corresponding to the prediction of storage modulus (G′), loss modulus (G″), complex viscosity (η∗), and loss factor (tan δ), respectively. Quantitatively, SVM (Support Vector Machine) regression outperformed Partial Least Squares (PLS) with Rc2 values (0.95, 0.94, 0.94) and Rp2 values (0.93, 0.93, 0.94) for G′, G″, and η∗. NIR spectra-based predictive models demonstrated superior performance compared to rheo-fermentation properties models. In summary, these findings show the potential of NIR spectroscopy as a rapid tool for predicting the fermentation progress of 3D-printed doughs.

Transfer of near-infrared analysis models for gasoline RON based on ensemble learning

The research octane number (RON) has guiding significance for evaluating the quality of gasoline, while near-infrared (NIR) spectroscopy analysis technology provides an important means for the detection of RON non-destructively and rapidly. When using a near-infrared spectrometer to obtain the RON of gasoline, if the analysis model can be shared among different instruments, it will greatly reduce the cost of re-modeling or model maintenance. Aiming to achieve the sharing of a NIR spectroscopy analysis model for RON between two portable near-infrared spectrometers of the same model, two ensemble learning algorithms, random forest (RF) and extreme gradient boosting (XGBoost), were employed for investigation, as well as two other machine learning algorithms, support vector regression (SVR) and decision tree (DT). Based on the RON of 120 gasoline samples and their NIR spectroscopy collected on the two spectrometers, hybrid and pure models were established to evaluate their sharing performance among SVR, DT, RF and XGBoost. In order to further simplify the model and improve its robustness and prediction accuracy, the characteristic wavelength selection strategies, including elimination of uninformative variables (UVE), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), were also adopted to optimize the model. The results showed that the hybrid model based on the CARS-RF method yielded the best prediction performance, with the coefficient of determination (R2) of 0.96, 0.86, and 0.94 for the single prediction sets of instrument A, instrument B, and the hybrid prediction set of the two instruments, respectively. Therefore, the hybrid modeling method based on ensemble learning algorithms combined with an appropriate wavelength selection strategy can effectively improve the robustness and universality of the model, and achieve the sharing of gasoline RON models on two near-infrared spectrometers of the same model.

Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii.

Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.

The Effect of Drying Method on The Quality of MOCAF (Modified Cassava Flour) from Raw Material Beta-Carotene-Rich Bokor Genotype Cassava

The high dependence on flour imports is a significant challenge to overcome by processing local food ingredients through diversification. To address this challenge, the use of MOCAF (Modified Cassava Flour) as a substitute for wheat flour has been carried out in the manufacture of wet and dry noodles, along with analog rice, and bread. The optimization of MOCAF production from beta-carotene-rich cassava depends on the selection of appropriate equipment technology and drying process. Therefore, this study aimed to analyze the effect of drying method on MOCAF characteristics of beta-carotene-rich cassava genotype. The analysis was carried out using two methods, namely sun drying for 3-4 days and oven drying at 60 o C for 24 hours. Parameters analyzed included viscosity, solubility, syneresis, Near Infra-Red analysis, proximate analysis, and Scanning Electron Microscope (SEM). The results showed that drying affected the physicochemical properties of MOCAF, including viscosity, solubility, and syneresis. NIR (Near-infrared) and proximate analysis showed that drying process affected the decrease in moisture, ash, protein, and fat content, along with an increase in crude fiber, and dry fiber matter content. Furthermore, SEM microstructural analysis resulted in the reformation of starch granules, characterized by changes in morphology and structure, such as the separation of irregular spherical shapes, and hollowness.

Replacement of raw materials in the formulation of industrial products based on near-infrared spectroscopy and particle swarm optimization

The replacement of raw materials in formulations is a crucial but challenging issue in industrial production. The goal is to substitute the inadequate or costly raw materials or their combinations with the adequate or potentially adequate inventory but while still ensuring the consistency and stability of product quality. In the present study, near-infrared (NIR) technology is first used for rapid and non-destructive analysis and further comprehensive characterization of the chemical characteristics of different raw materials. Afterwards, the particle swarm optimization (PSO) method is modified for the processing of NIR data and formulation replacement. Under the conditions of optimal constraints, an integrated strategy for replacement and optimization of raw materials in formulation is fully established. This yields optimized results for replacements that meet real demands in formulation design. In addition, the simulation of NIR spectra is initially realized by combining multiple Gaussian functions, which constructs simulated datasets that are suitable for verifying the accuracy and reliability of the proposed method. The real example of the replacement of raw materials is applied to comprehensively demonstrate the effectiveness and capability of the strategies proposed in this study. Among the 3,000 combinations used for formulation replacement, the recommended results obtained through the PSO method exhibit a high targeted proportion, attaining 81.82%, in which the difference between the optimal and original formulation is less than 0.0005. The results of expert validation indicate that 83% of the evaluations are consistent with spectral similarity assessments, which shows that the replaced results effectively meet the requirements for objective, scientific and digitized requirements in product manufacturing. Furthermore, this proposed method is applicable to various analysis and application scenarios on the basis of NIR analysis and global optimization.

Evaluating resistance drilling and NIR spectroscopy for quantifying wood quality of young eucalypts grown in Guangxi province in southern China

Cost-effective, non-destructive means of estimating wood properties of eucalypts grown in Guangxi, China, were evaluated using near infrared (NIR) spectroscopy and resistance drilling. For this evaluation, wood samples for NIR analysis and resistance drill measurements were collected from 581 trees, representing eight plantation Eucalyptus taxa across five sites. The near infra-red (NIR) spectra collected from the 581 wood samples were analysed using existing calibrations for kraft pulp yield, cellulose content, hot water extractives and Klason lignin. These spectra and calibrations were effective in predicting over 60% of the variance in total carbohydrates and over 80% of the extractives and lignin values from independent laboratory analysis of a subset of 100 samples. Resistance drilling combined with software developed for processing the drill data proved to be a quick, low cost and effective means of quantifying wood density (as amplitude of the drill torque), stem diameter and bark thickness.

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis.The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60–70%. Conversely, this reduction in accuracy was relatively contained (10–15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts.The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Pulpwood holocellulose model transfer based on screening with consistent and stable signals algorithm.

In this study, in order to realize the sharing of the near-infrared analysis model of holocellulose between three spectral instruments of the same type, 84 pulp samples and their content of holocellulose were taken as the research objects. The effects of 10 pre-processing methods, such as 1st derivative (D1st), 2nd derivative (D2nd), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), autoscaling, normalization, mean centering and pairwise combination, on the transfer effect of the stable wavelength selected by screening wavelengths with consistent and stable signals (SWCSS) were discussed. The results showed that the model established by the wavelength selected by the SWCSS algorithm after the autoscaling pre-processing method had the best analysis effect on the two target samples. Root mean square error of prediction (RMSEP) decreased from 2.4769 and 2.3119 before the model transfer to 1.2563 and 1.2384, respectively. Compared with the full-spectrum model, the value of AIC decreased from 3209.83 to 942.82. Therefore, the autoscaling pre-processing method combined with SWCSS algorithm can significantly improve the accuracy and efficiency of model transfer and provide help for the application of SWCSS algorithm in the rapid determination of pulp properties by near-infrared spectroscopy (NIRS).

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.

Rapid quantitative analysis of natural indigo dye content using near-infrared spectroscopy

Natural indigo, the most widely produced and utilized natural dye, encounters quality challenges due to the lack of standardization in the natural dye industry. Rapid determination of natural indigo dye contents before the dyeing process appears extremely important. In this study, two prediction models for different concentrations were established using partial least squares in conjunction with near-infrared analysis quantitatively to analyze the natural indigo dye content. A total of 228 indigo samples were collected from 14 different dyestuffs across various regions, with concentrations ranging from 100 to 1000 mg/L and 10 to 100 mg/L, respectively. The spectral pre-processing methods of multiplicative scatter correction plus first-order derivative and Savitzky–Golay smoothing plus band normalization plus first-order derivative were selected to enhance the model prediction accuracy. The optimized model exhibited excellent prediction accuracy. Within the concentration range of 100–1000 mg/L, the model has an R2 value of 0.9994, and a root mean square error of prediction value of 6.36 mg/L. In the concentration range of 10–100 mg/L, the model returned an R2 value of 0.9907, and a root mean square error of prediction value of 2.80 mg/L. The model's detection limit stands at 49.2 mg/L. The results demonstrated that the near-infrared models developed in this study can be used rapidly and accurately for the quantitative determination of natural indigo dyes.

Rapid determination of chemical compositions of raw materials and intermediate products of lube base oil by near infrared spectroscopy combined with library spectral fitting method

Rapid analysis of the detailed hydrocarbon composition of vacuum gas oil, hydrocracking bottom oil, and hydrogenated lube base oil is a critical technology in the production of hydrogenated lube base oil. In this study, based on a quantity of representative samples, a novel approach for estimating the hydrocarbon composition for vacuum gas oil, hydrocracking bottom oil and hydrogenated lube base oil was developed by near infrared (NIR) spectra combined with new chemometric method called library spectra fitting. The proposed method can reduce the maintenance requirements of traditional multivariate calibration such as partial least squares (PLS) apparently. The results of NIR analysis are basically consistent with ASTM standard method, which can fulfill the needs of rapid analysis in industrial field. Currently, the technology and corresponding software tools have been applied in a petroleum refinery.

The Effect of Fermentation Time on The Quality of Mocaf (Modified Cassava Flour) with Raw Material Bokor Genotype Cassava

Cassava genotype Bokor has a fairly high content of beta carotene, which is beneficial for health and can also support the development of the functional food industry. MOCAF (Modified Cassava Flour) is a product made from cassava flour which is processed by modifying cassava cells through fermentation. This study aims to analyze the effect of long fermentation time on the characteristics of MOCAF from beta-carotene-rich cassava genotype Bokor. In this study, three variations of fermentation time were used, namely 15 hours, 24 hours and 40 hours. The research results showed that the time of fermentation affected the physicochemical properties of MOCAF. NIR analysis showed that the fermentation time affected the chemical composition of MOCAF. At 40 hours (last time of fermentation), there was a decrease in fat content with a value of 1.59% and an increase in water, protein and fibre content with a value of 13.71%, 3.94% and 2.28%, respectively. Proximate analysis showed that there was an effect of the length of fermentation time on the MOCAF content, namely at 40 hours, there was an increase in water content with a value of 10.10%, a decrease in dry matter, ash, protein, fat, fibre with a value of 89.90%, 0.60%, 0.78% respectively, 0.45%, and 1.29%. Scanning Electron Microscope (SEM) analysis shows the effect of fermentation time on the morphological properties of MOCAF starch granules. The longer the fermentation time, the more perforated or damaged the surface of the starch granules. Keywords: Fermentation time, MOCAF flour, NIR (Near Infrared), Proximate, SEM (Scanning Electron Microscope)

Sample selection method using near‐infrared spectral information entropy as similarity criterion for constructing and updating peach firmness and soluble solids content prediction models

AbstractWhen using near‐infrared (NIR) techniques for analysis, model construction and maintenance updates are essential. When model construction is performed in machine learning, the sample set is usually divided into the calibration set and the validation set. The representativeness of the calibration set and the reasonable distribution of the validation set affects the accuracy of the established model. In addition, when maintaining and updating models, selecting the most informative updated sample not only improves the model prediction accuracy but also reduces sample preparation. In this paper, the spectral information entropy (SIE) is proposed to be used as a similarity criterion for dividing the sample set and use this criterion to select updated samples. The Kennard–Stone (KS) and the sample set portioning based on joint x–y distance (SPXY) methods were used for comparison to verify the superiority of the proposed method. The results showed that the model built after dividing the sample set using the SIE method has good prediction effect compared with KS and SPXY method. When predicting soluble solid content (SSC) and hardness, the prediction determination coefficient ( ) was improved by more than 15%, and the root mean square error (RMSE) of prediction was reduced by 50%. In terms of model updating, selecting a small number of updated samples using the SIE method can improve the correlation coefficient ( ) to more than 80%, and updated models' prediction accuracy is higher than that of KS and SPXY method. It is confirmed that the SIE method can make the NIR analysis technique more reliable.

Non-neotissue constituents as underestimated confounders in the assessment of tissue engineered constructs by near-infrared spectroscopy

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.

The Effect of Variation of Types of Microbial Starters on The Physical Characteristics of Modified Cassava Flour (Mocaf) Rich in Beta-Carotene

<p>The development of beta-carotene-rich mocaf flour as a functional food aims to increase its nutritional content and functional properties as an antioxidant for health. The development of beta-carotene-rich mocaf flour can be optimized through good fermentation methods, especially with the use of starter. This study aims to further analyze the effect of using a type of starter on the mocaf characteristics of cassava species rich in beta-carotene through a comparison of several types of starter. In this study, three types of starter were used, namely the Bimo-CF starter, the Berhasil starter and thse BRIN starter. The results showed that the use of starter during fermentation affected the physicochemical properties of mocaf flour in the form of differences in solubility, increased viscosity and decreased syneresis. Scanning Electron Microscope (SEM) was used to analyze the microstrutural of mocaf flour using starter during fermentation resulted in a more massive reshuffling of starch granules and causes changes in the structure of starch granules that become irregular round in shape with increasingly non-uniform sizes. NIR (Near infrared) and proximate analysis of the content of mocaf flour, showed that the use of the Bimo-CF starter, the Berhasil starter and the BRIN starter during fermentation affected the decrease in moisture content, protein content, ash content, fiber content, phosphorus, and increased fat and dry matter content.</p>

Study on marine microplastics monitoring based on infrared spectroscopy technology

In recent years, microplastics particles have been detected in many sea areas around the world. Microplastics has done great harm to marine and terrestrial seawater ecosystems, so it is necessary to obtain the effective statistical data of microplastics in the environment accurately and quickly for the further study of pollution in microplastics. In this paper, based on IR (Infrared Spectroscopy) technology, hyperspectral images of marine microplastics samples containing different materials were obtained. SVM (Support Vector Machine) algorithm is used to identify microplastics in hyperspectral images. The results show that the microplastics abundance ranges from 5.193 to 20.281 N/L, 6.087 to 38.679 N/L and 7.498 to 11.084 N/L, respectively, and the average abundance is 11.83 N/L, 24.84 N/L and 19.27 N/L, respectively. The types of microplastics in the bottom water of the bay in the study area are mainly fibers (53–68%) and debris (23–34%). NIR (Near Infrared) analysis shows that the characteristic curves of microplastics spectra of the same species with different particle sizes are different. IR technology combined with chemometrics algorithm has great potential for the detection of microplastics in seawater surface and seawater. This method is simple and feasible, and has the feasibility of popularization.

Rapid Characterization of Male and Female Taxus chinensis by Near-Infrared (NIR) Spectroscopy and High-Performance Liquid Chromatography (HPLC) with Orthogonal Partial Least Squares—Discriminant Analysis (OPLS-DA)

Taxus chinensis var. mairei (T. chinensis) is a dioecious plant that is important in traditional Chinese medicine. However, information about male and female T. chinensis is absent. To establish a simple, rapid, and reliable method for the qualitative identification of male and female T. chinensis, near-infrared spectroscopy (NIR) and high-performance liquid chromatography (HPLC) with the orthogonal partial least-squares discriminate analysis (OPLS-DA) mode was performed. NIR and discriminant analysis (DA) were employed for the first time to distinguish male and female T. chinensis. Meanwhile, HPLC also was optimized to determine the contents of the bioactive ingredients 10-deacetyltaxol (10-DAT) and paclitaxel of T. chinensis. Herein, the results of the validation showed that the DA model using NIR had a correct differentiation of nearly 100% for male and female T. chinensis. Moreover, the HPLC results indicated that the content of paclitaxel and 10-DAT was higher in female plants than in males. Additionally, based on the HPLC content results of male and female T. chinensis, the OPLS-DA successfully distinguished male and female T. chinensis. Thus, combining HPLC and OPLS-DA to initially differentiate between males and females allowed further quantitative and qualitative analysis of male and female T. chinensis. This method provides an approach to the identification and quality control of traditional Chinese medicine.

Combining Near-Infrared (NIR) Analysis and Modelling as a Fast and Reliable Method to Determine the Authenticity of Agarwood (Aquilaria spp.)

The resinous wood produced by the Aquilaria and Gyrinops species—agarwood—is both rare and highly valuable. It is used in products from perfumes to medicines and has an estimated global market value of $32 billion. As a result, the adulteration and illegal purchasing of agarwood is widespread and of specific concern to enforcement agencies globally. Therefore, it is of interest to have a fast, reliable, and user-friendly method to confirm the authenticity of a sample of agarwood. We investigated the use of near infrared (NIR) data to develop a method that rapidly distinguished between authentic and non-authentic agarwood samples, based upon a soft independent model of class analogy (SIMCA), using software specific to the application of infrared data to material authentication. The model showed a clear distinction between the authentic and non-authentic samples. However, the small values involved led to poor automatic validation results.