Handheld NIR Spectrometers Research Articles

Primary investigation of an innovative method for milk authenticity using two handheld spectrometers and chemometrics: Comparison with portable ATR-FTIR

This study advocates the use of handheld spectrometers, spanning the visible-near-infrared (Vis-NIR, 400–1000 nm) and near-infrared (NIR, 900–1700 nm) ranges, alongside a portable attenuated total reflection-Fourier transform infrared (ATR-FTIR) instrument, for discerning water adulteration in raw bovine milk. Samples with water adulteration levels ranging from 0 % to 30 % underwent direct analysis without additional preparation. Principal component analysis was utilized for differentiation, while quantification was achieved using partial least squares regression (PLSR) and radial basis function-artificial neural network (ANN) models. The ATR-FTIR instrument displayed remarkable performance, achieving regression coefficients of prediction (R2p) of 0.99 and 0.99, with root mean square error of prediction (RMSEP) values of 0.20 % and 0.53 % in PLS and ANN regression models, respectively. Similarly, handheld NIR spectrometer yielded robust results, with R2p values of 0.99 and 0.99 and RMSEP values of 1.28 % and 1.12 % in PLS and ANN models, respectively. Vis-NIR spectrometer also demonstrated commendable performance, with R2p values of 0.92 and 0.94 and RMSEP values of 4.53 % and 4.83 % in PLS and ANN models, respectively. ATR-FTIR showed the best performance followed by handheld NIR and Vis-NIR spectrometers. All instruments can be used potentially for screening and/or quality control in the dairy industry.

Nutrient quantification of livestock slurry and digestate using a low-cost handheld NIR spectrometer and multi-biomass calibration models

Real-time analysis during slurry field application can potentially ensure higher efficiency in crop fertilisation and reduce related environmental problems. Near Infrared Spectroscopy (NIRS) can predict quickly and adequately the nutrient content of the slurry but the devices are still costly. In this study, a low-cost portable device has been tested to predict total solids, total Kjeldahl nitrogen, total ammonia nitrogen, and phosphorus contents in cattle and pig slurries and digestate. Multivariate calibration models were developed for different biomass types (multi-biomass) by comparing support vector machine (SVM) and partial least square (PLS) regressions. The developed SVM and PLS models showed satisfactory and similar results, with performance to deviation ratio (RPD) values ranging between 2.22 and 2.80. The use of the handheld device with the obtained models also meets the requirements of a certification protocol for commercial NIR sensors and therefore can ensure a better slurry management at farm level.

Application of handheld NIR spectrometer for simultaneous identification and quantification of quality parameters in intact mango fruits

Application of handheld NIR spectrometer for simultaneous identification and quantification of quality parameters in intact mango fruits

Near-infrared spectroscopy for structural bone assessment

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp). NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R2 = 0.91, outer R2 = 0.83), thickness (Tb.Th, inner R2 = 0.9, outer R2 = 0.79), and cortical thickness (Ct.Th, inner and outer both R2 = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality. We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use.

Classification of bloodstains deposited at different times on floor tiles using hierarchical modelling and a handheld NIR spectrometer.

Bloodstains are commonly encountered at crime scenes, especially on floor tiles, and can be deposited over different periods and intervals. Therefore, it is crucial to develop techniques that can accurately identify bloodstains deposited at different times. This study builds upon a previous investigation and aims to enhance the performance of three distinct hierarchical models (HMs) designed to differentiate and identify stains of human blood (HB), animal blood (AB), and common false positives (CFPs) on nine different types of floor tiles. Soft Independent Modeling Class Analogies (SIMCA), and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed as decision rules in this process. The originally published model was constructed using a training set that included samples with a known time of deposit of six days. This model was then tested to predict samples with various deposition times, including human blood samples aged for 0, 1, 9, 20, 30, and 162 days, as well as animal blood samples aged for 0, 1, 10, 13, 20, 29, 105, and 176 days. To improve the identification of human blood, the models were modified by adding zero-day and one-day-old bloodstains to the original training set. All models showed improvement when fresher samples were included in the training set. The best results were achieved with the hierarchical model that used partial least squares-discriminant analysis as the second decision rule and incorporated one-day-old samples in the training set. This model yielded sensitivity values above 0.92 and specificity values above 0.7 for samples aged between zero and 30 days.

Avaliação não destrutiva de atributos de qualidade em maçãs e peras usando espectroscopia no infravermelho próximo e quimiometria

Abstract The objective of this study was to evaluate the performance of a handheld NIR spectrometer for non-destructive quality analysis of apples and pears produced in the Brazilian Semi-arid region. NIR spectra were acquired with a portable spectrometer in the wavelength range of 750–1065 nm and reference analyses of dry matter content (DMC) and soluble solids content (SSC) were measured weekly during 10 weeks of storage at 0.5 °C. Spectra were pre-processed with standard normal variate and used to develop DMC and SSC models using partial least squares regression with full cross-validation. The models were validated using data not included in the calibration. Satisfactory prediction results were obtained for SSC in apples (R² = 0.58) and pears (R² = 0.55), and for DMC in apples (R² = 0.55) and pears (R² = 0.65). All prediction models showed a relative root mean square error of prediction lower than 8%. These findings indicate that the NIR spectrometer is a promising tool to be used for a rapid and non-destructive determination of internal quality traits in apples and pears.

Feasibility Study on the Use of a Portable NIR Spectrometer and Multivariate Data Analysis to Discriminate and Quantify Adulteration in Fertilizer

The rise in population growth worldwide requires efficient management of agricultural lands through the correct determination of authentic fertilizers. In this current study, a rapid on-site detection technique was developed by using portable NIR spectroscopy in the wavelength range of 740–1070 nm together with optimum multivariate algorithms to identify fertilizer integrity (unexpired, expired, and adulterated) as well as quantify the levels (10–50%) of adulteration. NIR models were built based on support vector machine (SVM) and random forest (RF) for identification, while different types of partial least square regression (PLS, iPLS, Si-PLS, and GaPLS) were used for quantification purposes. The models were evaluated according to identification rate (Rt), coefficient of correlation in prediction (Rpre2), and root mean square error of prediction (RMSEP). For the identification of the integrity of the fertilizer, among the mathematical pretreatments used, the first derivative (FD) together with SVM gave above 99.20% identification rate in both calibration and prediction sets. For the quantification of the adulterants, Si-PLS was found to be superior and showed an excellent predictive potential of Rpre2 = 0.95–0.98 and RMSEP = 0.069–0.11 for the two fertilizer types used. The overall results indicated that a handheld NIR spectrometer together with appropriate algorithms could be employed for fast and on-site determination of fertilizer integrity.

Post-Mortem Interval of Human Skeletal Remains Estimated with Handheld NIR Spectrometry.

Simple SummaryHandheld NIR spectrometry represents a non-destructive method to estimate post-mortem interval with a short sample-preparation time. Based on a deep-learning technique for post-mortem interval approach (ranging from 1 day up to 2000 years) an estimation of post-mortem interval with an accuracy of almost 100% in bones is possible.Estimating the post-mortem interval (PMI) of human skeletal remains is a critical issue of forensic analysis, with important limitations such as sample preparation and practicability. In this work, NIR spectroscopy (NIRONE® Sensor X; Spectral Engines, 61449, Germany) was applied to estimate the PMI of 104 human bone samples between 1 day and 2000 years. Reflectance data were repeatedly collected from eight independent spectrometers between 1950 and 1550 nm with a spectral resolution of 14 nm and a step size of 2 nm, each from the external and internal bone. An Artificial Neural Network was used to analyze the 66,560 distinct diagnostic spectra, and clearly distinguished between forensic and archaeological bone material: the classification accuracies for PMIs of 0–2 weeks, 2 weeks–6 months, 6 months–1 year, 1 year–10 years, and >100 years were 0.90, 0.94, 0.94, 0.93, and 1.00, respectively. PMI of archaeological bones could be determined with an accuracy of 100%, demonstrating the adequate predictive performance of the model. Applying a handheld NIR spectrometer to estimate the PMI of human skeletal remains is rapid and extends the repertoire of forensic analyses as a distinct, novel approach.

Benchtop NIR data standardization on handheld spectrometers to identify paracetamol tablets

AbstractNear‐infrared spectroscopy (NIRS) allows innovative applications in terms of quality control, for example, in raw materials verification, in process analytical technology (PAT), and in discrimination between genuine and falsified medicines. The development of small and cheap handheld devices is expanding in the field, while trying to keep similar performances as benchtop instruments have. Considering traceability and quality control of drug compounds, this work is intended to identify 13 different paracetamol tablets on the Belgian market by using NIRS. The performances of a Fourier‐transform near‐infrared spectroscopy (FT‐NIR) benchtop and two handheld NIR spectrometers were investigated comparatively. All spectra were collected through the blister in the reflectance diffuse mode. NIR spectral fingerprints were pretreated and analyzed using principal component analysis (PCA) and classified with soft independent modeling of class analogy (SIMCA). The performances of the spectrometers were evaluated after standardization of the reference benchtop database to the handheld spectrometers. The instrumental response of the benchtop spectrometer was standardized towards the handheld device using the piecewise direct standardization (PDS) algorithm. These investigations permitted the advantages and limitations of NIR data standardization on predictive models to be pointed out. The SIMCA models based on the spectral data of the benchtop (B), handheld 1 (H1), and handheld 2 (H2) instruments had an accuracy of 99.2%, 97.7%, and 96.2%, respectively. The same accuracy was obtained with the models of the transferred benchtop spectra for the H1 and H2 devices. However, adding handheld spectra to the transferred benchtop spectra resulted in models with improved accuracy: from 97.7% to 98.5% for the H1 instrument and from 96.2% to 97.7% for the H2 instrument. The proposed strategy highlights the potential of using a reference database collected by NIRS for pharmaceutical analyses by handheld NIR spectrometers in terms of traceability.

Performance of three handheld NIR spectrometers for predicting grass silage quality

Description of the subject. Feed is the main variable cost in dairy farming. More efficient use of forage resources is one way to reduce production costs. Improving forage resource efficiency can start with a better assessment of the dry matter content and nutritional value of forages. Currently, analytical process time is often long and analyses are not repeatable while the quality of the fodder changes over time. Being able to analyze forages directly on-farm would make it possible to adapt the animal diet according to forage variability, in order to improve the profitability of the farm. Objectives. To propose in situ rapid analysis solutions to better characterize dry matter content and the chemical composition of fodder for assessing its feeding value. Method. The performance of three recently developed spectroscopic handheld devices, namely the Viavi’s MicroNIR 1700, the Ocean Insight’s FlameNIR and the Malvern Panalytical’s ASD FieldSpec 4, are evaluated to predict dry matter content and the chemical composition of fresh and unground grass silage in the framework of precision feeding and compared to the reference benchtop Foss’s XDS instrument. The conventional global PLS and local PLS are used as multivariate calibration methods. Results. The assessed handheld devices allow the dairy farmer to obtain a relatively precise quantitative prediction of the dry matter and crude fiber content (2.5% and 1.8% respectively on average, in terms of ratios between the local PLS error on fresh forage and the reference method error) in order to adapt the livestock diet. Crude protein, even if the prediction accuracy is lower (6.4%), is still well predicted. Higher errors are obtained for ash (9.2%), crude neutral (6.8%) and acid detergent fiber (6.9%). Conclusions. The studied devices should allow the dairy farmer to obtain a relatively precise quantitative prediction of those quality parameters in order to directly adapt the quantity of forage distributed to the animals. Performances could probably be improved by including more samples/spectra into the databases.

Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques.

The global market for organic cocoa beans continues to show sturdy growth. A low-cost handheld NIR spectrometer (900-1700 nm) combined with multivariate classification algorithms was used for rapid differentiation analysis of organic cocoa beans' integrity. In this research, organic and conventionally cultivated cocoa beans were collected from different locations in Ghana and scanned nondestructively with a handheld spectrometer. Different preprocessing treatments were employed. Principal component analysis (PCA) and classification analysis, RF (random forest), KNN (K-nearest neighbours), LDA (linear discriminant analysis), and PLS-DA (partial least squares-discriminant analysis) were performed comparatively to build classification models. The performance of the models was evaluated by accuracy, specificity, sensitivity, and efficiency. Second derivative preprocessing together with PLS-DA algorithm was superior to the rest of the algorithms with a classification accuracy of 100.00% in both the calibration set and prediction set. Second derivative algorithm was found to be the best preprocessing tool. The identification rates for the calibration set and prediction set were 96.15% and 98.08%, respectively, for RF, 91.35% and 92.31% for KNN, and 90.38% and 98.08% for LDA. Generally, the results showed that a handheld NIR spectrometer coupled with an appropriate multivariate algorithm could be used in situ for the differentiation of organic cocoa beans from conventional ones to ensure food integrity along the cocoa bean value chain.

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles

One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.

Canola quality by handheld NIR spectrometer

Handheld NIR spectrometers can be used to predict some of the most important compounds that define canola quality (high oil and protein contents, low glucosinolate and chlorophyll contents, and fatty acid composition). It is important to test models on true external verification sets instead of relying only on cross-validation for model stability. For handheld NIR spectrometers, the compact design necessarily has small sample windows, necessitating multiple measurements in intact seeds to reduce sampling error. Ground/milled seeds or flour samples have lesser sampling effect. It is important to understand how the limited wavelength range of some handheld NIR spectrometers makes NIR spectrometers unsuitable for the prediction of certain quality parameters.

Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production

This work aimed to evaluate the performance of a handheld NIR spectrometer in developing calibration models to quantify brix and pol at various stages of an industrial sugar production process. Because of sample variability, collected over two harvesting seasons, NIR measurements were acquired either in transmittance or diffuse reflectance. For modelling purpose, partial least squares (PLS), also combined with variable selection techniques, and support vector machine regression (SVR) were investigated. SVR was applied to handle non-linearities within the data. In general, results illustrated the best performance of SVR, that yielded lower root mean square error of prediction (RMSEP) values for brix and pol for spectra acquisition in transmittance (0.59 and 0.69%w/w) and using diffuse reflectance (1.44 and 2.44%w/w), respectively. Results from models using spectra collected in transmittance were comparable to those reported in other works where benchtop instruments were used, highlighting the cheaper and simpler employment of the portable spectrometer.

A cheap handheld NIR spectrometric system for automatic determination of methane, ethane, and propane in natural gas and biogas

In this study, a new NIR detection system using a cheap handheld NIR spectrometer, and a tungsten lamp coupled to a lab-made NIR flow cell was developed for the successful automatic quantification of methane, ethane, and propane in natural gas and biogas samples. To prepare standard gaseous mixtures for full-spectrum PLS multivariate calibration modeling and to record the NIR spectra of these mixtures and samples, this NIR detection system was connected to a flow automatic system and a gas chromatograph. The raw and second-derivative NIR spectra of the calibration set were then employed to construct PLS models using full cross-validation leave-one-out. All models presented high correlation coefficients (>0.9) and root mean square error (<5% mol mol−1) values. However, the RMSEP values using raw and preprocessed datasets were statistically similar according to the F-test at the 95% confidence level. The advantage of using raw data is the elimination of the preprocessing step for future prediction of samples to be analyzed. Once the NIR detection system has been previously calibrated via an automatic or batch process, it can be used to monitor light hydrocarbons or other gases inline (as in pipelines).

Rapid evaluation on pharmacodynamics of Curcumae Rhizoma based on Micro-NIR and benchtop-NIR

It’s far from enough to describe the blood circulation promoting effect of Curcumae Rhizoma (CR), which is widely grown as a functional vegetable or spice in south and southeast Asian countries, and processed Curcumae Rhizoma (PCR), only by disclosing the content of a couple of relative compounds. In this study, the thrombin inhibitory effect as well as 2,2′-azino-bis (3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-picrylhydrazyl (DPPH) free radical scavenging ability of CR/PCR extracts was investigated, and TANGO Fourier transform near-infrared (FT-NIR, Bruker, Germany)—a benchtop instrument allowing the full range NIR wavelength scanning—and handheld-NIR spectrometer (Micro-NIR, NIR-S-G1, InnoSpectra corporation, China) that can be connected to smart phones were used to realize the rapid detection of pharmacodynamic indicators. model was evaluated based on the determination coefficient (R2), root mean square error (RMSE), standard error of test set (SEP) and ratio of performance to deviation (RPD). The results of pharmacodynamics experiment confirmed for the first time that CR has significant inhibitory effect on thrombin, and the modeling results revealed that Micro-NIR had a good prediction on the antioxidant capacity (ABTS and DPPH free radical clearance) with RPD greater than 3, but showed a general predictive performance on thrombin inhibition ability (RPD = 2.434). In contrast, FT-NIR provided a good prediction for all the three indicators, with R2 greater than 0.9 and RPD greater than 4.5. Further insights into the capability of the two devices were obtained by analyzing the wavebands selection work. In the full wavelength range, wavebands related to thrombin inhibition were mainly distributed in the combination area which is out of the reach of handheld Micro-NIR, thus resulting in a decrease in the prediction ability. Therefore, compared to the benchtop-NIR, the detection range of the handheld-NIR is the main factor limiting its capability Based on an overall assessment, handheld NIR spectrometer, by greatly expanding the application scenario of NIR technology, is considered as a useful device with a satisfying predictive ability through model construction.

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS).

The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350–2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R2 ≤ 0.98 for N, P, K, Ca, Mg and S) and most micronutrients (0.64 ≤ R2 ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

Quantification of Silymarin in Silybi mariani fructus: Challenging the Analytical Performance of Benchtop vs. Handheld NIR Spectrometers on Whole Seeds.

The content of the flavonolignan mixture silymarin and its individual components (silichristin, silidianin, silibinin A, silibinin B, isosilibinin A, and isosilibinin B) in whole and milled milk thistle seeds (Silybi mariani fructus) was analyzed with near-infrared spectroscopy. The analytical performance of one benchtop and two handheld near-infrared spectrometers was compared. Reference analysis was performed with HPLC following a Soxhlet extraction (European Pharmacopoeia) and a more resource-efficient ultrasonic extraction. The reliability of near-infrared spectral analysis determined through partial least squares regression models constructed independently for the spectral datasets obtained by the three spectrometers was as follows. The benchtop device NIRFlex N-500 performed the best both for milled and whole seeds with a root mean square error of CV between 0.01 and 0.17%. The handheld spectrometer MicroNIR 2200 as well as the microPHAZIR provided a similar performance (root mean square error of CV between 0.01 and 0.18% and between 0.01 and 0.23%, respectively). We carried out quantum chemical simulation of near-infrared spectra of silichristin, silidianin, silibinin, and isosilibinin for interpretation of the results of spectral analysis. This provided understanding of the absorption regions meaningful for the calibration. Further, it helped to better separate how the chemical and physical properties of the samples affect the analysis. While the study demonstrated that milling of samples slightly improves the performance, it was deemed to be critical only for the analysis carried out with the microPHAZIR. This study evidenced that rapid and nondestructive quantification of silymarin and individual flavonolignans is possible with miniaturized near-infrared spectroscopy in whole milk thistle seeds.

Fast determination of three components in milk thistle extract with a hand-held NIR spectrometer and chemometrics tools

Milk thistle extract that is widely supplied in the market has three main active ingredients, including silybin, silychristin and silydianin (SS), and isosilybin, they have a lot of valuable functions for the human body. The determination of these active ingredients is of great significance for customers, so it is urgent to develop convenient, fast, and on-site analytical methods. In this study, we developed a fast method to detect the active ingredients using a hand-held NIR spectrometer MicroNIR 1700 to record spectra and applied multiple pretreatment methods to process them. The random frog (RG) method was used to optimize wavelength variables with high information, and the partial least square (PLS) regression was used to calibrate the spectra data of silybin, SS, and isosilybin. The results showed that the standard normal variate transformation (SNV), SNV+ first derivative, and first derivative were the best pretreatment methods for silybin, SS, and isosilybin. The calibration performances of selected wavelength variables were significantly improved compared to the whole wavelength variables. The the minimum limit of quantification (LOQmin) of the silybin, SS, and isosilybin were 9.53%, 10.86%, and 2.64%, respectively. For the validation of new unknown samples, the root mean squared error of prediction (RMSEP) of silybin, SS, and isosilybin were 0.666%, 0.510%, and 0.157%, and the corresponding R2 were 0.992, 0.898, and 0.936, respectively, it demonstrated that the proposed methods are accurate and robust. Benefiting from the portable hand-held NIR spectrometer and the reliable algorithm, the present method offers a promising approach for the determination of the active ingredients in milk thistle extract because it is rapid, convenient, non-polluting, and on-site, does not require professional skills and expensive apparatus.

Authenticity Identification of Copaiba Oil Using a Handheld NIR Spectrometer and DD-SIMCA

Copaiba oil (CO) is a non-timber forest product, obtained from the Copaifera genus, used in popular medicine, pharmaceutical, and cosmetic industries. The increasing demand and price of this oil also increased the occurrence of adulterations in commercial products and small providers in cooperatives. Nowadays, the GC-MS is the most suitable method used for quality control. However, the high cost of this technique is unfeasible for small traders and cooperatives. Considering the lack of alternative methods for CO analysis, this work presents an easy, direct, fast, and inexpensive method based on a handheld NIR spectrometer and a DD-SIMCA model for purity authentication. The training set was composed of 65 pure CO samples originated by 12 distinct sources of Copaifera sp. oil, 9 distinct commercial samples, binary and ternary mixtures among them. The validation was accomplished using 213 samples constituted by independent authentic samples, samples submitted to thermal stress, commercial samples with and without edible vegetable oil adulteration, and samples purposefully adulterated with edible oils (palm, olive, sunflower, coconut, and soybean). The optimized DD-SIMCA model showed 95% of sensitivity regarding the target class, where 5% the false-negative rate revealed the necessity of increasing the number of commercial samples in the training set. The specificity, related to samples not belonging to the target class, was 97.3%, showing that the model is highly effective to detect adulterated samples. The average efficiency of the method was 92.3%, revealing to be very promising for quality control and authenticity testing applications in small cooperatives.