Handheld Near-infrared Spectrometer Research Articles

Miniaturized Near-Infrared (MicroNIR) Spectrometer in Plastic Waste Sorting.

Valorisation of the urban plastic waste in high-quality recyclates is an imperative challenge in the new paradigm of the circular economy. In this scenario, a key role in the improvement of the recycling process is exerted by the optimization of waste sorting. In spite of the enormous developments achieved in the field of automated sorting systems, the quest for the reduction of cross-contamination of incompatible polymers as well as a rapid and punctual sorting of the unmatched polymers has not been sufficiently developed. In this paper, we demonstrate that a miniaturized handheld near-infrared (NIR) spectrometer can be used to successfully fingerprint and classify different plastic polymers. The investigated urban plastic waste comprised polyethylene (PE), polypropylene (PP), poly(vinyl chloride) (PVC), poly(ethylene terephthalate) (PET), and poly(styrene) (PS), collected directly in a recycling plastic waste plant, without any kind of sample washing or treatment. The application of unsupervised and supervised chemometric tools such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) on the NIR dataset resulted in a complete classification of the polymer classes. In addition, several kinds of PET (clear, blue, coloured, opaque, and boxes) were correctly classified as PET class, and PE samples with different branching degrees were properly separated.

Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits

The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

Investigations into the use of handheld near-infrared spectrometer and novel semi-automated data analysis for the determination of protein content in different cultivars of Panicum miliaceumL.

A set of 42 millet (panicum miliaceum L.) samples was investigated for its protein content using standard Kjeldahl analysis and near-infrared spectroscopy. The performance of three handheld spectrometers was compared to a benchtop instrument. The used spectrometers operate in different regions of the NIR, which gives interesting insights into the applicability of each region. Additionally, semi-automated, consumer-oriented multivariate data analysis was compared to sophisticated data evaluation.The performance of the near-infrared instruments was compared using important statistical parameters of the established cross- and test set validated partial least squares regression (PLS-R) models. Milled and intact samples were analysed, in order to further evaluate the importance of homogeneity.The results showed that the benchtop spectrometer is capable of accurately analysing protein content of millet grains, with root mean square error (RMSEP) values for milled and intact grains of approximately 0.5%. Two PLS-R models of handheld instruments also yielded good results for milled grains with RMSEP values of about 0.6%. The semi-automated multivariate data analysis showed some drawbacks compared to standard data processing software. For intact grains, however, similar results could be achieved.

Handheld near-infrared spectrometer for on-line monitoring of biodiesel production in a continuous process

In this work, the use of a handheld near-infrared spectrometer (MicroNIR) for on-line monitoring of biodiesel production in a continuous process is described. The process consists of a laboratory scale reactive distillation column where transesterification reactions between cottonseed oil and ethanol take place. This column can operate with different ethanol to oil molar ratios. In the present study, a severe condition using an excess of ethanol at the point monitored with NIRS was applied. This condition caused problems in modelling the content of the alkyl ester, particularly because of the broad and intense band related to the ethanol in the spectral region covered by MicroNIR. Therefore, for process monitoring and control, both an estimate of the alkyl ester as well as of ethanol contents were carried out. To deal with this, PLS calibration models were developed to estimate concentrations of ethanol and alkyl ester at the bottom of the column. In addition, in order to estimate the yield of the alkyl ester, a PLS model to assess glycerol content was also developed. In spite of the problematic conditions under which the column operated, mainly related to the excess of ethanol, the models presented satisfactory results for ethanol (RMSEP = 1.66 wt%), alkyl ester (RMSEP = 2.15 wt%) and glycerol (RMSEP = 1.08 wt%) quantifications and have demonstrated the feasibility of the handheld instrument to monitor biodiesel production in a continuous process.

Evaluation of the performance of three hand-held near-infrared spectrometer through investigation of total antioxidant capacity in gluten-free grains

The performance of three portable NIR spectrometers was compared by analysing the total antioxidant capacity (TAC) of different species of gluten-free grains. TAC is often used to evaluate the quality of foods and was determined using Folin-Ciocalteu measurements and used as reference data for establishing PLS-R models with NIR data. NIRS enables fast and non-invasive measurements.The microPhazir RX and the MicroNIR 2200 are broadly used in chemical and pharmaceutical industries, whereas SCiO is a pocket-sized, consumer-oriented spectrometer. The devices work in different regions of the NIR spectrum and their performances was compared using statistical parameters.77 samples were measured and analysed using the software The Unscrambler X, as well as SCiO-Lab. All models established were cross- and test set validated. The multivariate data processing using The Unscrambler X yielded similar results as SCiO-Lab. The best model was established for non-milled samples measured with the MicroNIR 2200 and analysed using The Unscrambler X.

Feasibility of a handheld near infrared device for the qualitative analysis of bloodstains

One of the most common tasks in criminal investigation is to determine from which tissue source a biological fluid stain originates. As a result, there are many tests that are frequently used to determine if a stain is blood, semen or saliva by exploiting the properties of certain molecules present within the fluids themselves. These include chemical reagents such as the Kastle-Meyer or Acid Phosphatase tests, as well as other techniques like the use of alternative light sources. However, most of the tests currently available have some major drawbacks. In this study, a handheld near-infrared spectrometer is investigated for the specific identification of deposited bloodstains. First, a calibration was carried out by scanning over 500 positive (blood present) and negative (blood absent) samples to train several predictive models based on machine learning principles. These models were then tested on over 100 new positive and negative samples to evaluate their performance. All models tested were able to correctly classify deposited stains as blood in at least 81% of tested samples, with some models allowing for even higher classification accuracy at over 94%. This suggests that handheld near infrared devices could offer great opportunity for the rapid, low cost and non-destructive screening of body fluids at scenes of crime.

Sensors break out of the lab

Sensors break out of the lab

Evaluation and identification of blood stains with handheld NIR spectrometer

This work describes a noninvasive, nondestructive methodology for the confirmatory and in situ identification of dry blood stains deposited in different substrates using a hand-held near infrared (NIR) spectrometer. Different supervised pattern recognition methods were evaluated and compared for the correct identification of human blood. Human and animal blood stains and stains from different commercial products (perceived as common false positives) that had been deposited on different substrates were analyzed directly by NIR spectroscopy. The best pre-processing used was Standard Normal Variate (SNV) and normalization by range, which was obtained by evaluating the best classification for human blood with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA showed 100% correct classification for porcelain and glass substrates but 80% for metal and 90% for ceramic substrates. Genetic Algorithm - Linear Discriminant Analysis (GA-LDA) showed better classification performance (100%) than Successive Projection Algorithm - Linear Discriminant Analysis (SPA-LDA), in which one false positive and one false negative were obtained. Partial-Least Squares Discriminant Analysis (PLS-DA) correctly classified the human blood and other stains in all substrates.

Application of benchtop and portable near-infrared spectrometers for predicting the optimum harvest time of Verbena officinalis

This study examined the applicability of near-infrared (NIR) spectroscopy coupled with multivariate data analysis (MVA) to determine the ideal harvest time of Verbena officinalis. NIR analyses were performed non-invasively on the fresh plant material based on the quantification of the key constituents verbenalin and verbascoside. Vibrational spectroscopic measurements were performed applying a conventional NIR benchtop device as well as a laboratory independent handheld NIR spectrometer. A novel high performance liquid chromatography (HPLC) method was applied as a reference method. For both instruments partial least squares (PLS) regression models were established performing cross validations (CV) and test-set validations (TSV). Quality parameters obtained for the benchtop device revealed that the newly established NIR method enabled reliable quantifications of the main compounds verbenalin and verbascoside related to the dried and fresh plant material. The results of the miniaturised spectrometer revealed that accurate quantitative calibration models could be developed for verbascoside achieving a comparable prediction power to the benchtop device. PLS models for verbenalin were less precise suggesting the application of portable devices including a different spectral range and resolution. The work demonstrated the feasibility of NIR vibrational spectroscopy performing direct measurements on pharmaceutically relevant fresh plant material enabling a quick and simple determination of the ideal harvest time of Verbena officinalis.

A New Handheld Device for the Detection of Falsified Medicines: Demonstration on Falsified Artemisinin-Based Therapies from the Field.

Poor-quality medicines are a major problem for health-care systems in resource-poor settings as identifying falsified medicines requires a complex laboratory infrastructure such as a Medicines Quality Control Laboratory. We report here an evaluation of a low-cost, handheld near-infrared spectrometer (NIRS) device by analyzing a library of artemisinin-based combination therapy (ACT) medicines to determine its usefulness as a drug-screening tool. The “SCiO” research prototype device was used to collect NIR spectra of a library of ACT and artesunate monotherapy medicine samples previously collected in Bioko Island and Equatorial Guinea and Kintampo, Ghana. The quality of these samples had been categorized as falsified, substandard, and quality assured based on the amount of stated active pharmaceutical ingredients detected using high-performance liquid chromatography photodiode array. Numerical analyses were performed on the NIR spectra to assess the usefulness of NIR to identify falsified and substandard medicines. The NIRS device was successful at detecting falsified medicines in all cases where the library contained both quality assured and falsified medicines of the same stated brand of medicines. The NIRS device was successful at identifying substandard amounts of artesunate but not amodiaquine in the ACT samples (N = 15) of artesunate–amodiaquine. This work reveals that this low-cost, portable NIRS device is promising for screening ACTs for falsified samples and could enable widespread drug screening at all points of the health system.

Comparative Variable Temperature Studies of Polyamide II with a Benchtop Fourier Transform and a Miniature Handheld Near-Infrared Spectrometer Using 2D-COS and PCMW-2D Analysis.

The main objective of this communication is to compare the performance of a miniaturized handheld near-infrared (NIR) spectrometer with a benchtop Fourier transform near-infrared (FT-NIR) spectrometer. Generally, NIR spectroscopy is an extremely powerful analytical tool to study hydrogen-bonding changes of amide functionalities in solid and liquid materials and therefore variable temperature NIR measurements of polyamide II (PAII) have been selected as a case study. The information content of the measurement data has been further enhanced by exploiting the potential of two-dimensional correlation spectroscopy (2D-COS) and the perturbation correlation moving window two-dimensional (PCMW2D) evaluation technique. The data provide valuable insights not only into the changes of the hydrogen-bonding structure and the recrystallization of the hydrocarbon segments of the investigated PAII but also in their sequential order. Furthermore, it has been demonstrated that the 2D-COS and PCMW2D results derived from the spectra measured with the miniaturized NIR instrument are equivalent to the information extracted from the data obtained with the high-performance FT-NIR instrument.

Spectra Transfer Between a Fourier Transform Near-Infrared Laboratory and a Miniaturized Handheld Near-Infrared Spectrometer.

The aim of this contribution is to demonstrate the transfer of spectra that have been measured on two different laboratory Fourier transform near-infrared (FT-NIR) spectrometers to the format of a handheld instrument by measuring only a few samples with both spectrometer types. Thus, despite the extreme differences in spectral range and resolution, spectral data sets that have been collected and quantitative as well as qualitative calibrations that have been developed thereof, respectively, over a long period on a laboratory instrument can be conveniently transferred to the handheld system. Thus, the necessity to prepare completely new calibration samples and the effort required to develop calibration models when changing hardware platforms is minimized. The enabling procedure is based on piecewise direct standardization (PDS) and will be described for the data sets of a quantitative and a qualitative application case study. For this purpose the spectra measured on the FT-NIR laboratory spectrometers were used as "master" data and transferred to the "target" format of the handheld instrument. The quantitative test study refers to transmission spectra of three-component liquid solvent mixtures whereas the qualitative application example encompasses diffuse reflection spectra of six different current polymers. To prove the performance of the transfer procedure for quantitative applications, partial least squares (PLS-1) calibrations were developed for the individual components of the solvent mixtures with spectra transferred from the master to the target instrument and the cross-validation parameters were compared with the corresponding parameters obtained for spectra measured on the master and target instruments, respectively. To test the retention of the discrimination ability of the transferred polymer spectra sets principal component analyses (PCAs) were applied exemplarily for three of the six investigated polymers and their identification was demonstrated by Mahalanobis distance plots for all polymers.

Near Infrared Spectral Assessment of Stay-Green Barley Genotypes under Heat Stress

This research aimed to correlate near infrared (NIR) spectral data to physiological and biochemical responses associated with stay-green (SG) traits expression in barley ( Hordeum vulgare L.) plants experiencing heat stress. One hundred lines were randomly sub-sampled from a doubled haploid ND24260 × Flagship population consisting of 334 lines. A glasshouse trial with partial sample duplication was grown under terminal heat stress to induce SG expression during grain-fill. The “greenness” of the first leaves under the flag leaf (FL-1) was assessed using NIR spectra. A handheld NIR spectrometer was used to understand and describe some of the physiological and biochemical mechanisms and responses related to SG expression and vice versa which cannot be observed using visual assessments. The use of NIR spectroscopy made it possible both to differentiate between cosmetic (changes in pigments with senescence of spike but no functional chlorophyll affects) and functional (effects on chlorophyll catabolism) SG expression and also to differentiate between the two groups of functional SG. The delayed onset and reduced rate of leaf senescence was linked to plant moisture (water) and plant maturity, which is dependent on the level of SG expression. Variance in the dominant water peak at 1450 nm in the NIR spectrum can be used to differentiate between cosmetic and functional SG expression. The spectral data from the leaves showed significant correlation, with protein ( R2 = 0.62) and starch ( R2 = 0.70) composition of the grain. The use of NIR spectroscopy allows for the rapid, non-destructive analysis of leaves; enabling multiple traits to be assessed with a single measurement. Understanding the relationship between spectra and SG expression may help develop NIR spectroscopy as a rapid, high-throughput methodology for phenotyping breeding populations, with a view to improve drought resistant barley cultivars.

Moisture content in raw rubber sheet analyzed by transflectance near infrared spectroscopy

Moisture content is an important trait for rubber sheet trading system. Therefore, a calibration equation for predicting moisture content was created by near infrared (NIR) technique in order to develop a more fair trading system in Thailand. Spectra were recorded in two systems. One was measurement on each rubber sheet and the other was on a pile of sheets. Both were measured by a handheld NIR spectrometer in the short wavelength region (700–1100 nm) in the transflectance mode using Teflon as a diffuse reflector. The spectra showed the peak at about 900 nm which belongs to isoprene, the major component of rubber sheet. Pretreatment with second derivative was applied to remove baseline shift effect occurring due to thickness differences on each rubber sheet. From validation results, moisture contents predicted by single sheet system were more accurate than a pile of sheet system with standard error of prediction (SEP) = 0.39% and bias of -0.07%, and they were not significantly different from the actual values at 95% confidence. As a result, determining moisture content in each rubber sheet by a handheld NIR spectrometer provided accurate values, easy and rapid operation.