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.

A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples.

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

Feasibility of near-infrared spectroscopy for species identification and parasitological diagnosis of freshwater snails of the genus Biomphalaria (Planorbidae).

Near Infrared Spectroscopy (NIRS) has been applied in epidemiological surveillance studies of insect vectors of parasitic diseases, such as the Dengue's mosquitoes. However, regarding mollusks, vectors of important worldwide helminth diseases such as schistosomiasis, fascioliasis and angiostrongyliasis, NIRS studies are rare. This work proposes to establish and standardize the procedure of data collection and analysis using NIRS applied to medical malacology, i.e., to mollusk vectors identifications. Biomphalaria shells and live snails were analyzed regarding several operational aspects, such as: moisture, shell side and position of the live animal for acquisition of NIR spectra. Representative spectra of Biomphalaria shells and live snails were collected using an average of 50 scans per sample and resolution of 16 cm-1. For shells, the sample should first be dried for a minimum of 15 days at an average temperature of 26±1°C, and then placed directly in the equipment measurement window with its left side facing the light beam. Live animals should be dried with absorbent paper; placed into a glass jar, and analyzed similarly to the shells. Once standardized, the technique was applied aiming at two objectives: identification of Biomphalaria using only the shells and parasitological diagnosis for Schistosoma mansoni infection. The discrimination of the three Biomphalaria species intermediate hosts of S. mansoni only by shell has technical limit due to the scarcity of organic material. Nevertheless, it was possible to differentiate B. straminea from B. tenagophila and B. glabrata with 96% accuracy. As for the parasitological diagnosis, it was possible to differentiate infected mollusks shedding S. mansoni cercariae from the non-infected ones with 82, 5% accuracy. In conclusion, the Near Infrared Spectroscopy (NIR's) technique has proven to be an innovative and sound tool to detect infection by S. mansoni in the different species of Biomphalaria intermediate hosts.

Field Application of NIR Spectroscopy for the Discrimination of the Biomphalaria Species That Are Intermediate Hosts of Schistosoma mansoni in Brazil.

Near Infrared Spectroscopy (NIRS) is a spectroscopic technique that evaluates the vibrational energy levels of the chemical bonds of molecules within a wavelength range of 750-2,500 nm. This simple method acquires spectra that provide qualitative and quantitative data on the chemical components of the biomass of living organisms through the interaction between the electromagnetic waves and the sample. NIRS is an innovative, rapid, and non-destructive technique that can contribute to the differentiation of species based on their chemical phenotypes. Chemical profiles were obtained by NIRS from three snail species (Biomphalaria glabrata, Biomphalaria straminea, and Biomphalaria tenagophila) that are intermediate hosts of Schistosoma mansoni in Brazil. The correct identification of these species is important from an epidemiological viewpoint, given that each species has distinct biological and physiological characteristics. The present study aimed to develop a chemometric model for the interspecific and intra-specific classification of the three species, focusing on laboratory and field populations. The data were obtained from 271 live animals, including 150 snails recently collected from the field, with the remainder being raised in the laboratory. Populations were sampled at three localities in the Brazilian state of Rio de Janeiro, in the municipalities of Sumidouro (B. glabrata) and Paracambi (B. straminea), and the borough of Jacarepaguá in the Rio de Janeiro city (B. tenagophila). The chemometric analysis was run in the Unscrambler® software. The intra-specific classification of the field and laboratory populations obtained accuracy rates of 72.5% (B. tenagophila), 77.5% (B. straminea), and 85.0% (B. glabrata). The interspecific differentiation had a hit rate of 75% for the field populations and 80% for the laboratory populations. The results indicate chemical and metabolic differences between populations of the same species from the field and the laboratory. The chemical phenotype, which is closely related to the metabolic profile of the snails, varied between environments. Overall, the NIRS technique proved to be a potentially valuable tool for medical malacology, enabling the systematic discrimination of the Biomphalaria snails that are the intermediate hosts of S. mansoni in Brazil.

Monitoring the quality of ethanol-based hand sanitizers by low-cost near-infrared spectroscopy.

The use of near-infrared spectroscopy (NIRS) based on a low-cost portable instrument for monitoring the quality of the three major formulations of ethanol-based hand sanitizers used for prevention against CoVID-19 disease is described. The quality of the sanitizers was evaluated using two approaches. In the first, a qualitative method was developed to identify gross non-conformities, using NIR spectral data compression by principal components analysis and projection of the spectrum of the tested sample in the principal component space delimited by samples of sanitizers prepared in the laboratory. In the second, a quantitative method was designed to determine the active substance (ethanol) employing multivariate regression based on partial least squares. The results demonstrate that the first approach can be used to detect non-conformities in the sanitizer composition, mostly associated with incorrect ethanol content. The second explores the use of NIRS for determination of the ethanol content in the three formulations aiming the quality control of the sanitizer manufacturing process. The ethanol content can be determined with an absolute root mean square error of prediction (RMSEP) equal to 0.68% (m/m), 0.83% (m/m) and 1.0% (v/v) for the three formulations evaluated. The RMSEP was estimated as 1.3% (m/m) for the commercial products. The measurement protocol takes approximately 1 min and requires only about 120 µL of a sample. Besides, NIRS was employed to compare the rate of volatilization of the ethanol in the different formulations, an important parameter concerning the efficacy of ethanol-based sanitizers.

Determination of the isotopic composition of enriched materials using laser ablation molecular isotopic spectrometry: partial least squares and multivariate curve resolution for the determination of 15N content in enriched urea.

A quantitative analytical method based on laser ablation molecular isotopic spectrometry (LAMIS) and multivariate analysis was developed and evaluated for the determination of the isotopic composition of enriched materials. The method consists preparing a concentrated solution of the enriched material, using small quantities of a sample (125 mg), and ensuring the economic efficiency of the analysis. Standard solutions of known isotopic contents are prepared by employing mixtures of urea highly enriched in 15N and urea of natural isotopic ratio and analyzed by mass spectrometry. A small volume (30 µL) of these solutions is delivered to a filter paper disc (3 cm diameter). After drying, the disc, offering a homogeneously distributed analyte, is presented to a LAMIS equipment to acquire the vibronic emission spectra containing information about the isotopologues of interest. To illustrate the proposed method, the content of 15N is determined in enriched samples of urea. In this case, each spectrum is normalized by the intensity of emission of the CN isotopologues for the electronic (Δν = 0) emission band at 387.1 nm, ensuring better accuracy. Selected regions and single wavelengths of the vibronic emission spectrum (Δν = + 1 or - 1) related to CN species were employed to construct multivariate partial least squares (PLS) and univariate regression models to predict the isotopic content of new samples. Besides, the LAMIS data set was evaluated by multivariate curve resolution (MCR) algorithm. The best MCR and PLS models presented similar results regarding the accuracy to determine 15N content in enriched urea. MCR is capable of identifying spectral interferences and minimizing its effect. The results show that the proposed method based on LAMIS and PLS or MCR multivariate analysis can determine the 15N content in the range 5-50% with a root mean square error of prediction (RMSEP) respectively equal to 0.5 or 0.7% (m/m) in comparison with reference results obtained by mass spectrometry. Graphical abstract.

Evaluation of a low-cost portable near-infrared spectrophotometer for in situ cocaine profiling.

The potential of a low-cost (∼US$ 1000) portable near-infrared (NIR) spectrophotometer for in situ characterization of seized cocaine samples was evaluated. A set of 240 samples of cocaine seized in several regions and cities across Brazil by its federal police was employed in this study. These samples were previously analyzed by chromatography at the Forensic Chemistry Laboratory of the National Institute of Criminalistics in Brasília-DF for the contents of several constituents to chemically characterize the samples. A low-cost NanoNIR spectrophotometer (Texas instruments) was used to acquire the NIR spectra of the samples in the range 900-1700 nm. The spectra set was treated by the second derivative to construct and validate multivariate regression (Partial Least Square - PLS) and classification (software independent modeling of class analogy - SIMCA) models aiming to characterize the samples. Consequently, an informative toll for objective decision making could be used by the police agents to produce immediate answers to forensic questions raised at the point of seizing. Among those questions the most relevant are: does the seized sample contain cocaine? what is the cocaine form? what is its content? is the sample adulterated and/or diluted? what is the content of adulterant? is the sample significantly adulterated and/or diluted? what is the degree of oxidation of the cocaine? The results of this work allow to propose a NIR/chemometrics based analytical protocol providing fast answers to these questions with satisfactory confidence level for the purpose of reliably screen the seized samples.

Evaluating the potential of near infrared hyperspectral imaging associated with multivariate data analysis for examining crossing ink lines.

The determination of the chronological sequence of crossing ink lines is a recurrent issue in the forensic examination of questioned documents. This manuscript intends to evaluate the potential of hyperspectral imaging in the near infrared range (HSI-NIR) combined with multivariate data analysis for rapid, objective and nondestructive analysis of crossing ink lines made with black pens. Twenty-one black gel and ballpoint pens from different brands and models were employed to prepare crossing combinations in both orders in white office paper. An initial pre-selection and extraction step using the Principal Components Analysis (PCA) scores plot arranged as histograms was necessary for extracting the inks spectra. Then, Partial Least Squares-Discriminant Analysis (PLS-DA) was applied for selection of the most important variables. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in the augmented form was performed using both the raw and the pre-processed spectra. However, the pre-processing of spectra resulted in incorrect identification of pixels in the inks distribution maps. The MCR-ALS results obtained using the raw spectra allowed the correct determination of the order of crossings in only one pair of gel-gel pen crossings. The remainder were either impossible to discriminate ink from paper or the skipping of one pen ink line led to incorrect determinations in one of the orders. In spite of the practical advantages of the HSI-NIR for document examination and the different chemometric approaches considered for surpassing some of the spectral limitations, the results obtained demonstrate the difficulties of using this technology for application in real forensic cases.

Surface-enhanced Raman spectroscopy and MCR-ALS for the selective sensing of urinary adenosine on filter paper.

Adenosine is a purine nucleoside that is present in all human cells and is essential for regulating certain physiological activities in tissues and organs. Since adenosine is considered to be a potential cancer biomarker in urine, its determination may be crucial for the early diagnosis and non-invasive monitoring of cancer. Herein, we present a label-free method to quantify urinary adenosine using surface-enhanced Raman spectroscopy (SERS) and multivariate curve resolution-alternating least squares (MCR-ALS). Ring-oven preconcentration and direct deposition of monodisperse gold nanoparticles on filter paper were employed to improve the sampling efficiency. Further, MCR-ALS (assessed with and without a correlation constraint), the standard addition method and pH controls were combined to compensate for the matrix effect and to address overlapping bands in the analysis of human urine samples. As a result, the proposed method showed to be sensitive (LOD varying between 3.8 and 4.9 µmol L-1, S/R = 3), reproducible (RSD less than ±â€¯15%), and selective over other nucleosides (guanosine, cytidine, thymidine and uridine) and unknown interferences (second-order advantage). This is the first report of a SERS-chemometric method applied to urinary adenosine sensing at physiologically relevant concentrations, with minimal sample preparation, and has strong potential to be a valuable tool in cancer research.

Coupling of the ring-oven-based preconcentration technique and surface-enhanced Raman spectroscopy: Application for the determination of purine bases in DNA.

In this paper, we present the advantages and limitations of the coupling of a ring-oven-based preconcentration technique and surface-enhanced Raman spectroscopy (SERS). Three different methods to promote analyte adsorption on gold nanoparticles using crystal violet as a probe molecule were assessed. The results showed significant improvements in sampling process, selectivity, sensitivity, repeatability (less than ± 10%), and detection limits (nanomolar level) using a sample volume as small as 300 µL. Finally, the standard addition method was successfully applied to the quantitative SERS detection of adenine and guanine in calf thymus DNA after ring-oven preconcentration with a calculated value of (G + C)/(A + T) close to the literature value. This work could therefore pave the way to quantifying a wide variety of biologically relevant compounds in real-world samples via the use of a biodegradable, low-cost and disposable paper platform for SERS.

Multivariate quantification of mebendazole polymorphs by terahertz time domain spectroscopy (THZ-TDS).

This work presents an analytical method based on terahertz-time domain spectroscopy (THz-TDS) and partial least squares (PLS) regression models to quantify mebendazole (MBZ) polymorphs (forms A, B and C) in pharmaceutical raw material. Mebendazole polymorphs A, B and C were quantified with RMSEP values of 1.5% w/w, 1.2% w/w and 1.8% w/w, respectively. The limits of detection (LOD) ranges obtained with the best PLS regression models were 2.7-4.3% w/w, 2.9-4.0% w/w and 2.4-3.1% w/w, for polymorphs A, B and C, respectively. This analytical performance is better than those for the methods described in the literature using near (NIR) and middle (MIR) infrared spectroscopies. The main advantage of THz spectroscopy is its ability to access directly information related to crystal lattices. According to the results, the developed method is a powerful technique for the quantification of MBZ polymorphs in raw material. This methodology can be implemented as a Process Analytical Technology (PAT) tool for quality control of pharmaceutical feedstock.

A Simple Device for Lens-to-Sample Distance Adjustment in Laser-Induced Breakdown Spectroscopy (LIBS).

A simple device based on two commercial laser pointers is described to assist in the analysis of samples that present uneven surfaces and/or irregular shapes using laser-induced breakdown spectroscopy (LIBS). The device allows for easy positioning of the sample surface at a reproducible distance from the focusing lens that conveys the laser pulse to generate the micro-plasma in a LIBS system, with reproducibility better than ±0.2 mm. In this way, fluctuations in the fluence (J cm-2) are minimized and the LIBS analytical signals can be obtained with a better precision even when samples with irregular surfaces are probed.

iHEART: a miniaturized near-infrared in-line gas sensor using heart-shaped substrate-integrated hollow waveguides.

A novel heart-shaped substrate-integrated hollow waveguide (hiHWG) was integrated with a near-infrared micro-spectrometer (µNIR) for sensing natural gases, resulting in an ultra-compact near-infrared gas sensing system - iHEART. The iHEART system was evaluated using two different µNIR spectrometers, and the performance was compared with a laboratory NIR spectrometer for gas analysis based on an acousto-optic tunable filter (AOTF). The spectral data were pre-processed using the 1(st) derivative Savitzky-Golay algorithm, and then used for establishing multivariate regression models based on partial least squares (PLS). The root mean square errors of prediction (RMSEPs) obtained for major components of natural gas with both iHEART systems were similar to those associated with the AOTF spectrophotometer combined with a conventional long-path measurement cell. It was demonstrated that the iHEART system has significant potential for the development of compact in-line gas sensing systems, thus facilitating monitoring of (petro)chemically relevant processes and products. However, the flexibility and modularity of the system also allows tailoring iHEART to a wide range of other relevant analytical measurement scenarios requiring short response times and minute gas sample volumes.

Determination of detergent and dispensant additives in gasoline by ring-oven and near infrared hypespectral imaging.

A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.

Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber.

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 µm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Near infrared hyperspectral imaging for forensic analysis of document forgery.

Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928-2524 nm with spectral and spatial resolutions of 6.3 nm and 10 µm, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares-Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identification.

Determination of cellulose crystallinity by terahertz-time domain spectroscopy.

Terahertz-time domain spectroscopy (THz-TDS) has the ability to probe the crystallinity of several materials, due to the interaction of THz radiation with optical phonons in crystal lattices. In this work, THz-TDS has been used to quantify the degree of crystallinity of microcrystalline cellulose (MCC) samples. The THz spectra of cellulose present absorption features which could be directly correlated with the crystallinity index (CI) obtained by means of the well-established powder X-ray diffraction (PXRD) technique. The effect of THz time-domain signal processing was investigated, and both univariate and multivariate, based on partial least-squares (PLS), regressions were carried out with the signal in the frequency domain to correlate the THz spectra with CI. Results show that the multivariate regression models based on spectral data, collected with the sample displaced from the focal plane of the THz optics to improve representativeness and measurement repeatability, present the best performance with external validation achieving an absolute root-mean-square error of prediction (RMSEP) of 4% for CI. This result compares well with the PXRD technique.

A new approach to polarimetric measurements based on birefringent crystals and diode lasers.

A new polarimetric instrument and measurement method is described based on the use of diode lasers as radiation source (532, 650 and 1064nm) and birefringent prisms, such as Glan-Laser and Wollaston, as analyzers. The laser radiation is passed through a dichroic polarizer film for further orientation of its polarization plane at 45° in relation to the polarization plane of the analyzer. The polarized beam, oriented in that way, passes the sample cell, impinges the prism surface, and the intensities of the two emerged beams are detected by two twin silicon detectors. Ideally, in the absence of any optically active substances, the crystals produces two orthogonally polarized refracted beams of equal intensity. In the presence of an optically active substance, the arctangent of the square root of the beam intensities ratio is equal to the new polarization angle (ß) of the laser beam. The rotation angle imposed for any optically active substance present in the sample cell is then given by: α=(45-ß)°. Because the rotation is obtained by the ratio of the intensities of two beams, it is independent of the laser intensity, which can vary up to ±15% with no significant effect on the accuracy of the polarimetric measurement. The instrument has been evaluated for measurement of optically active substances such as sucrose and fructose. The instrument employs low cost components, is capable of attaining a repeatability of ±0.003° and can measure the rotation angle, over a ±45° range, in less than 2s. Because it does not present any moving parts it can be easily adapted for in/on-line process monitoring of optically active substances.

Ring-oven based preconcentration technique for microanalysis: simultaneous determination of Na, Fe, and Cu in fuel ethanol by laser induced breakdown spectroscopy.

The ring-oven technique, originally applied for classical qualitative analysis in the years 1950s to 1970s, is revisited to be used in a simple though highly efficient and green procedure for analyte preconcentration prior to its determination by the microanalytical techniques presently available. The proposed preconcentration technique is based on the dropwise delivery of a small volume of sample to a filter paper substrate, assisted by a flow-injection-like system. The filter paper is maintained in a small circular heated oven (the ring oven). Drops of the sample solution diffuse by capillarity from the center to a circular area of the paper substrate. After the total sample volume has been delivered, a ring with a sharp (c.a. 350 µm) circular contour, of about 2.0 cm diameter, is formed on the paper to contain most of the analytes originally present in the sample volume. Preconcentration coefficients of the analyte can reach 250-fold (on a m/m basis) for a sample volume as small as 600 µL. The proposed system and procedure have been evaluated to concentrate Na, Fe, and Cu in fuel ethanol, followed by simultaneous direct determination of these species in the ring contour, employing the microanalytical technique of laser induced breakdown spectroscopy (LIBS). Detection limits of 0.7, 0.4, and 0.3 µg mL(-1) and mean recoveries of (109 ± 13)%, (92 ± 18)%, and (98 ± 12)%, for Na, Fe, and Cu, respectively, were obtained in fuel ethanol. It is possible to anticipate the application of the technique, coupled to modern microanalytical and multianalyte techniques, to several analytical problems requiring analyte preconcentration and/or sample stabilization.

A low cost short wave near infrared spectrophotometer: application for determination of quality parameters of diesel fuel.

A low cost absorption spectrophotometer for the short wave near infrared spectral region (850-1050 nm) is described. The spectrophotometer is basically composed of a conventional dichroic lamp, a long-pass filter, a sample cell and a Czerny-Turner type polychromator coupled to a 1024 pixel non-cooled photodiode array. A preliminary evaluation of the spectrophotometer showed good repeatability of the first derivative of the spectra at a constant room temperature and the possibility of assigning some spectral regions to different C-H stretching third overtones. Finally, the spectrophotometer was successfully applied for the analysis of diesel samples and the determination of some of their quality parameters using partial least squares calibration models. The values found for the root mean square error of prediction using external validation were 0.5 for the cetane index and from 2.5 to 5.0 degrees C for the temperatures achieved during distillation when obtaining 10, 50, 85, and 90% (v/v) of the distilled sample, respectively.