Standardization from a benchtop to a handheld NIR spectrometer using mathematically mixed NIR spectra to determine fuel quality parameters

Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Gasoline discrimination using infrared spectroscopy and virtual samples based on measurement uncertainty

In a previous work, we proposed a methodology for pair-wise discrimination of gasoline samples by creating virtual samples based on physicochemical assays or distillation curves. Satisfactory results were achieved, although specialist and specific apparatus (not commonly available at police laboratories) were required. The present study goes a step further and for the first time investigates the possibility of infrared (IR) spectroscopy to enable a virtual samples-based methodology for comparison of gasoline samples in pairs. IR spectroscopy feasibility for in situ applications is attractive for forensic investigations. The performances of one handheld NIR device and one dual-range (FT-NIR and FT-IR) benchtop spectrometer were evaluated. The estimation of uncertainty in infrared spectral measurement (needed to generate virtual samples) is barely discussed in literature. So far, there are no literature reports describing quantification and comparison of measurement uncertainties for the spectral acquisitions evaluated here, especially regarding their use for generating virtual samples. A stepwise procedure to quantify uncertainties associated with IR spectral acquisition, at each wavenumber, is described. This method can be useful for understanding both the sources of variability in IR measurements and the system under investigation. Uncertainty estimation was based on experimental data and considered intermediate precision, repeatability and variations in sample temperature as sources of variability. Virtual samples were employed in a discrimination approach using SIMCA models. Results for portable NIR, FT-NIR and FT-IR data sets showed complete discrimination for 96.3%, 93.4% and 93.7% of the 1431 pairs of gasoline samples evaluated, respectively. These results were comparable and similar to those obtained for the physicochemical properties data set (95.7%), although slightly inferior to the result obtained for distillation curves (99.2%). Using IR non-destructive methods in this case could enable faster investigations and simpler analysis, especially for the low-cost handheld spectrometer. In a screening approach, atmospheric distillation assays can be employed only if infrared techniques are not capable of distinguishing the samples subject to comparison. In this work, a pair of samples was considered to be completely discriminated only when a null false positive error (FPR) was achieved, although a more flexible criterium may be acceptable in practice. Finally, the methodology could be extended to other applications where sample comparison is important.

Quality Monitoring of Biodiesel and Diesel/Biodiesel Blends: A Comparison between Benchtop FT-NIR versus a Portable Miniaturized NIR Spectroscopic Analysis

A methodology such as near-infrared (NIR) spectroscopy, which enables in situ and in real-time analysis, is crucial to perform quality control of biodiesel, since it is blended into diesel fuel and the presence of contaminants can hinder its performance. This work aimed to compare the performance of a benchtop Fourier Transform (FT) NIR spectrometer with a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) to detect and quantify contaminants in biodiesel and biodiesel in diesel. In general, good models based on principal component analysis-linear discriminant analysis (PCA-LDA) of FT-NIR spectra were obtained, predicting with high accuracies biodiesel contaminants and biodiesel in diesel (between 75% to 95%), as well as good partial least square (PLS) regression models to predict contaminants concentration in biodiesel and biodiesel concentration in diesel/biodiesel blends, with high coefficients of determination (between 0.83 and 0.99) and low prediction errors. The miniNIR prototype’s PCA-LDA models enabled the prediction of target contaminants with good accuracies (between 66% and 86%), and a PLS model enabled the prediction of biodiesel concentration in diesel with a reasonable coefficient of determination (0.68), pointing to the device’s potential for preliminary analysis of biodiesel which, associated with its potential low cost and portability, could increase biodiesel quality control.

Performance Optimization of a Developed Near-Infrared Spectrometer Using Calibration Transfer with a Variety of Transfer Samples for Geographical Origin Identification of Coffee Beans

This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies-Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67-79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively.

Calibration with virtual standards for monitoring biodiesel production using a miniature NIR spectrometer

This work describes the use of virtual standards as calibration samples in an innovative multivariate calibration approach for the on-line monitoring of alkyl-esters content during biodiesel production process using a miniature near infrared (NIR) spectrometer. For comparison purposes, a partial least squares (PLS) model was built using synthetic blends prepared in laboratory with different concentrations of oil, glycerol, biodiesel, and ethanol and resulted in a satisfactory predictive ability (root mean square error of prediction, RMSEP, of 1.51% w/w). When compared to conventional methods, calibration with synthetic blends has the advantage of simplifying the experimental procedure and reducing the need for reference analysis. Nevertheless, it still requires the preparation of a considerable number of blends in laboratory. To overcome this limitation, this study proposed an innovative approach where a PLS model was constructed based on virtual standards: representative calibration spectra were created by mathematically mixing spectra from pure components and performing an adjustment using the Piecewise Direct Standardization (PDS) method. This significantly reduced the need for calibration synthetic blends and led to similar results (RMSEP of 1.75% w/w), compared to the previous approach. This work also demonstrates the use of the constructed models to predict the concentration profiles of alkyl-esters during the batch transesterification process.

Transfer learning strategy for plastic pollution detection in soil: Calibration transfer from high-throughput HSI system to NIR sensor

Rapid detection tasks in soil environment are generally implemented by various spectrometers and chemometric models. To reduce costs for model construction, calibration transfer from laboratory spectral instruments to portable devices has recently received extensive attention. In different application cases of model transference, most conventional methods require extra time to tune hyperparameters and to select calibration transfer techniques. Based on the near-infrared (NIR) analytical technique, this work aimed at exploring a transfer learning strategy to detect plastic pollution levels in the soil by transferring the model from a high-throughput hyperspectral image (HSI) system to an ultra-portable NIR sensor. Transfer learning was explored to diagnose the proper calibration transfer algorithm and construct the transferable model. For transferable model construction, conventional calibration transfer algorithms (Direct Standardization (DS) or Repeatability file (Repfile)) served as a pre-processing step, and non-parametric transfer learning algorithm (Easy Transfer Learning (EasyTL)) was explored in the modeling step. Supporting vector machine (SVM) was carried out as a typical modeling algorithm for comparison. For transformation algorithms selection, a distance metric algorithm, maximum mean discrepancy (MMD), was performed on spectral feature matrices before and after DS or Repfile transformation. On three transfer tasks, the results indicated that the Repfile-EasyTL model was a promising solution with higher accuracy, much lower time costs, less parameters, and dependency on the increase of standard samples than other models (SVM, DS-SVM, Repfile-SVM, EasyTL, DS-EasyTL). Moreover, MMD distance presented the great potential to serve as an indicator to vote the optimal calibration transfer algorithm before the modeling step.

Principles and Applications of Miniaturized Near-Infrared (NIR) Spectrometers

This review article focuses on the principles and applications of miniaturized near-infrared (NIR) spectrometers. This technology and its applicability has advanced considerably over the last few years and revolutionized several fields of application. What is particularly remarkable is that the applications have a distinctly diverse nature, ranging from agriculture and the food sector, through to materials science, industry and environmental studies. Unlike a rather uniform design of a mature benchtop FTNIR spectrometer, miniaturized instruments employ diverse technological solutions, which have an impact on their operational characteristics. Continuous progress leads to new instruments appearing on the market. The current focus in analytical NIR spectroscopy is on the evaluation of the devices and associated methods, and to systematic characterization of their performance profiles.

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.

Development of a "single-click" analytical platform for the detection of cannabinoids in hemp seed oil

In this work, an innovative screening platform is developed and validated for the on site detection of cannabinoids in hemp seed oil, for food safety control of commercial products. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that permits processing samples in a rapid and accurate way and to obtain in a single click the early detection of a residual amount of cannabinoids in oil, including cannabidiol (CBD), the psychoactive Δ9-tetrahydrocannabinol (THC) and the Δ9-tetrahydrocannabinolic acid (THCA). Simulated samples were realized to instruct the platform and prediction models were developed by chemometric analysis of the NIR spectra using partial least square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and on websites. Validation of the system was achieved by comparing results with those obtained from GC-MS analyses and a good correlation was observed.

The detection of cannabinoids in veterinary feeds by microNIR/chemometrics: a new analytical platform

In this work, the capabilities of a novel miniaturized and portable microNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of cannabinoids in veterinary feeds. In order to develop a predictive model that could identify and simultaneously quantify the residual amounts of cannabinoids, specimens from popular veterinary feeds were considered and spiked with increasing amounts of cannabidiol (CBD), Δ9-tetrahydrocannabinol (THC), and cannabigerol (CBG). Partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSr) were applied for the simultaneous detection and quantification of cannabinoids. The results demonstrated that the microNIR/chemometric platform could statistically identify the presence of CBD, THC and CBG in the simulated samples containing cannabinoids from 0.001 to 0.01%w/w, with the accuracy and sensitivity of the official reference methods actually proposed. The method was checked against false positive and true positive responses, and the results proved to be those required for confirmatory analyses, permitting to provide a fast and accurate method for monitoring cannabinoids in veterinary feeds.

Real time detection of amphetamine in oral fluids by MicroNIR/Chemometrics

In this work, a novel coupled approach MicroNIR/Chemometrics based on a miniaturized and portable spectrometer is proposed for the on site detection of amphetamines (AMP) in non pretreated oral fluids. In particular, the coupling of MicroNIR with chemometrics was investigated with the aim of developing a fast and accurate approach able to perform the on-site prediction of AMP abuse. A predictive model to be used in real cases was developed by collecting specimens from volunteers and spiked samples with increasing amounts of AMP were prepared to optimize calibration. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLS) were involved to detect and quantify AMP. Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify AMP abuse in simulated oral fluid samples containing, with the accuracy and sensitivity of the actual proposed official reference methods. The method was checked against false positive and true positive response and results proved to be those required for confirmatory analyses. This method would permit to simplify AMP abuse monitoring for roadside drug testing or workplace surveillance and may be of help at first aid points.

MicroNIR/Chemometrics: A new analytical platform for fast and accurate detection of Δ9-Tetrahydrocannabinol (THC) in oral fluids

BACKGROUND

Δ⁹-Tetrahydrocannabinol (THC) is already considered one of the most addictive substances since an increasing number of consumers/abusers of THC and THC based products are observed worldwide. In this work, the capabilities of a novel miniaturized and portable MicroNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of Δ⁹-Tetrahydrocannabinol (THC) oral fluids without any pretreatment.

METHODS

Specimens from volunteers were collected in order to consider any sources of variability in the spectral response and spiked with increasing amount of THC in order to realize predictive models to be used in real cases. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) for the simultaneously detection and quantification of THC, were applied to baseline corrected spectra pre-treated by first derivative transform.

RESULTS

Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify THC abuse in simulated oral fluid samples containing THC from 10 to 100 ng/ml, with a precision and a sensitivity of about 1.51% and 0.1% respectively.

CONCLUSIONS

The coupling MicroNIR/Chemometrics permits to simplify THC abuse monitoring for roadside drug testing or workplace surveillance and provides the rapid interpretation of results, as once the model is assessed, it can be used to process real samples in a "click-on" device.