FT-NIR Analysis Research Articles

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa.

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Evaluation of physico-chemical changes and FT-NIR spectra in fresh egg pasta packed in modified atmosphere during storage at different temperatures

Physico-chemical properties (i.e. water activity, colour, water absorption index and hardness) influence stability, quality and consumer acceptability of fresh pasta. The aim of this study was to investigate the effect of storage time on physico-chemical properties of fresh pasta stored at different temperatures (0, 5 and 10 °C). Storage time significantly affected the macroscopic properties (water absorption index and hardness) of fresh pasta and its water status (water activity, freezable water). During storage at all temperatures, water activity and freezable water decreased, confirming a reduction in water mobility in the product. Storage time increased pasta hardness and caused a reduction in the water absorption index. Fresh egg pasta retained its yellow colour, while lipid oxidation indices were only slightly affected. With the increase of storage temperature, the kinetic rate of water activity and gelatinisation degree decreased. In contrast, the kinetic rate of pasta hardness increased with the temperature. The Arrhenius relationship explained the temperature dependence of reaction constants, while FT-NIR analysis showed a structural reorganisation of pasta matrix during storage. Generally, pasta matrix reorganisation involved water, starch and proteins interactions, and storage temperature was an important factor affecting these modifications with specific effects on physico-chemical pasta properties (e.g. hardness).

FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals

Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.

Potential benefit for production management of a pulp and paper company from using an ft-nir analyzer

The pulp and paper segment has been seeking to increase mill efficiency through cost reduction and better production management. One of the ways to achieve this goal is through better control and operation of the pulp plant digester cooking. The study aimed to verify the accuracy of the FT-NIR analyzer in the liquor lines of a pulp and paper digester and its possible benefits for production management. The research methodology used was the case study, where it was analyzed and compared the results of the equipment with the values of the company's laboratory. The analysis of the instrument proved accurate and reliable, providing information on various properties of liquors that enable the optimization of production management with the stabilization of the operation. Future trends indicate that infrared technology will continue to play an important role as a detection platform due to its inherent flexibility and capability.

Predicting macronutrients and energy content of snack products using FT-NIR analysis and chemometric techniques

Nowadays, the consumption of snack products is permanently increasing. Because of the growing trend of snack consumption, it is more and more difficult to guarantee the quality and safety of the products. Near infrared spectroscopy (NIRS) method, combined with chemometric techniques provide outstanding solutions, due to its rapidity and simple sample preparation. The objective of this study was to investigate the possibilities of using NIRS to predict fat, protein, carbohydrate, sugar and salt content of all in all 155 commercially available snack products from 25 countries. The prediction models were performed using partial least squares regression (PLSR) with different spectral pre-processing methods. Different pre-processing methods proved to be the best to predict the five macronutrients, however, the final models showed good accuracy |R2/Q2 > 0.94/0.82|. The energy content of the samples was calculated from the measured parameters and interval PLS regression was accomplished to improve prediction parameters. The methods developed are suitable for analyzing snacks made from single or mixed raw materials.

Analysis of different forms Linderae Radix based on HPLC and NIRS fingerprints

Three different forms of Linderae Radix were evaluated by HPLC combined with NIRS fingerprint. The Linderae Radix was divided into three forms, including spindle root, straight root and old root. The HPLC fingerprints were developed, and then cluster analysis was performed using the SPSS software. The near-infrared spectra of Linderae Radix was collected, and then established the discriminant analysis model. The similarity values of the spindle root and straight root all were above 0.990, while the similarity value of the old root was less than 0.850. Two forms of Linderae Radix were obviously divided into three parts by the NIRS model and Cluster analysis. The results of HPLC and FT-NIR analysis showed the quality of Linderae Radix old root was different from the spindle root and straight root. The combined use of the two methods could identify different forms of Linderae Radix quickly and accurately.

Quantitative determination and classification of energy drinks using near-infrared spectroscopy.

Almost a hundred commercially available energy drink samples from Hungary, Slovakia, and Greece were collected for the quantitative determination of their caffeine and sugar content with FT-NIR spectroscopy and high-performance liquid chromatography (HPLC). Calibration models were built with partial least-squares regression (PLSR). An HPLC-UV method was used to measure the reference values for caffeine content, while sugar contents were measured with the Schoorl method. Both the nominal sugar content (as indicated on the cans) and the measured sugar concentration were used as references. Although the Schoorl method has larger error and bias, appropriate models could be developed using both references. The validation of the models was based on sevenfold cross-validation and external validation. FT-NIR analysis is a good candidate to replace the HPLC-UV method, because it is much cheaper than any chromatographic method, while it is also more time-efficient. The combination of FT-NIR with multidimensional chemometric techniques like PLSR can be a good option for the detection of low caffeine concentrations in energy drinks. Moreover, three types of energy drinks that contain (i) taurine, (ii) arginine, and (iii) none of these two components were classified correctly using principal component analysis and linear discriminant analysis. Such classifications are important for the detection of adulterated samples and for quality control, as well. In this case, more than a hundred samples were used for the evaluation. The classification was validated with cross-validation and several randomization tests (X-scrambling). Graphical Abstract The way of energy drinks from cans to appropriate chemometric models.

Determination of the Mechanical Properties of Rubber by FT-NIR

Mechanical tests, for example, tensile and hardness tests, are usually used to evaluate the properties of rubber materials. In this work, mechanical properties of selected rubber materials, that is, natural rubber (NR), styrene butadiene rubber (SBR), nitrile butadiene rubber (NBR), and ethylene propylene diene monomer (EPDM), were evaluated using a near infrared (NIR) spectroscopy technique. Here, NR/NBR and NR/EPDM blends were first prepared. All of the samples were then scanned using a FT-NIR spectrometer and fitted with an integration sphere working in a diffused reflectance mode. The spectra were correlated with hardness and tensile properties. Partial least square (PLS) calibration models were built from the spectra datasets with preprocessing techniques, that is, smoothing and second derivative. This indicated that reasonably accurate models, that is, with a coefficient of determination [R2] of the validation greater than 0.9, could be achieved for the hardness and tensile properties of rubber materials. This study demonstrated that FT-NIR analysis can be applied to determine hardness and tensile values in rubbers and rubber blends effectively.

Application of electrolyzed water for improving postharvest quality of mushroom

This paper focused on the effectiveness of electrolyzed water (EW) at different concentrations (5, 25, 50 and 100 mg/L) combined with passive atmosphere packaging on the quality of mushroom. In order to understand the effect of EW on mushrooms, gas composition inside packages, weight loss, pH, whiteness and browning index, texture profile analysis (TPA), cap development, electrolyte leakage and FT-NIR analysis were performed during the twelve days of storage at 4 °C. Samples washed with 25 and 50 mg/L EW consumed O2 lower than the other treatments. Mushrooms treated with 25 mg/L EW had a significantly lower electrolyte leakage values than untreated and 5 mg/L treated mushrooms. Mushrooms treated with 25 mg/L EW had the highest whiteness index and lowest browning index. EW treatments at the concentrations of 25 and 50 mg/L maintained the textural parameters and slowed down the weight loss better than other treatments. FT-NIR analysis supported the results obtained by weight loss and electrolyte leakage. In conclusion, the results of this research support the idea that combined use of EW treatment and passive modified atmosphere packaging can be used to extend the shelf life of mushrooms.

Critical examination of chemically modified hybrid thermosets: Synthesis, characterization and mechanical behavior in the plateau regime of polyaminosiloxane-nitrile-DGEBA

Poly(3-aminopropylmethylsiloxane) has been modified with acrylonitrile via aza Michael addition and a broad range of modified oligomers have been prepared with CN:NH2 ratio ranging between 0.1 and 1. NMR and FTnIR analysis reveals that acrylonitrile modification proceeds without formation of tertiary amines. Modified oligomers have been characterized by DSC and analysis of Tg reveals that the adducts are self-associated probably due to weak hydrogen bonding and dipole interactions. The modified oligomers with a modification degree higher than 0.4 were miscible with DGEBA. The low and high temperature relaxations of the cured thermosets have been measured by DMTA. In addition to the commonly observed β2 relaxation, a new β1 process linked by an isosbestic point to β2 has been found. β1 is attributed to an extended segment comprising the pendant propionitrile group as well as the aminopropyl segment that connects tertiary amines to the polysiloxane backbone. Elastic modulus as well as the α relaxation can be tuned from high Tg and high rubbery modulus to low Tg and high damping thermosets changing the nitrile content. The experimental network structure obtained from elastic measurements and the Tg were related through well established structure-property relations.

FT-NIR Analysis and Temperature Dependence of Different Italian Wine Samples

FT-NIR Analysis and Temperature Dependence of Different Italian Wine Samples

Biometric analysis of protein and oil contents of soybean genotypes in different environments

The objective of this work was to identify by biometric analyses the most stable soybean parents, with higher oil or protein contents, cultivated at different seasons and locations of the state of Minas Gerais, Brazil. Forty-nine genotypes were evaluated in the municipalities of Viçosa, Visconde do Rio Branco, and São Gotardo, in the state of Minas Gerais, from 2009 to 2011. Protein and oil contents were analyzed by infrared spectrometry using a FT-NIR analyzer. The effects of genotype, environment, and genotype x environment interaction were significant. The BARC-8 soybean genotype is the best parent to increase protein contents in the progenies, followed by BR 8014887 and CS 3032PTA276-3-4. Selection for high oil content is more efficient when the crossings involve the Suprema, CD 01RR8384, and A7002 genotypes, which show high mean phenotypic values, wide adaptability, and greater stability to environmental variation.

Fourier Transform Near-Infrared Spectroscopy to Predict the Gross Energy Content of Food Grade Legumes

The feasibility of Fourier transform near-infrared reflectance spectroscopy (FT-NIRS) for determining gross energy content in different food legumes has been investigated. Eighty food-grade legume samples were obtained from different retailers and local markets in Hungary and they included 42 common beans (Phaseolus vulgaris L.), 20 peas (Pisum sativum L.), 10 lentils (Lens culinaris L.), and 8 soya beans (Glycine max L.) both as full fat food and defatted. The samples were analyzed by an adiabatic bomb calorimeter and then scanned in a Bruker MPA FT-NIR Analyzer (800–2,500 nm). Two algorithms for spectral selection of calibration and validation samples, which represent variability encountered in the full population, were tested. Partial least squares regression were developed for the prediction of gross energy using four different spectral preprocessing methods (first and second derivative alone and combined with standard normal variation and multiplicative scatter correction). The results show that first derivative produced the most accurate results with very high coefficient of determinations in validation (<93 %) and with very low standard errors of validation (<0.025 kcal/g) as compared to the standard error of the reference method (0.204 kcal/g).

An innovative technique for extending shelf life of strawberry: Ultrasound

Ultrasound is one of the newest nonthermal methods to extend shelf life of fresh fruits during storage. The effectiveness of ultrasound depends on wave frequency, power and treatment time. The present study was designed to determine the effect of different ultrasound powers (30 W, 60 W, 90 W) and treatment times (5 min, 10 min) on quality of strawberry. Oxygen concentration inside packages of strawberries which treated with 30 W and 60 W ultrasound powers were higher than 90 W and control (CNT) groups. A sharp increase in CO2 release rate was observed for the CNT and 90 W treatments during the storage. Attributes such as pH, total soluble solid content and color for the 30 W and 60 W treatments were better than CNT and 90 W treatments. Decay incidence analysis confirmed that all ultrasound treatments were effective to reduce mold growth. The 30 W and 60 W treatments maintained better textural properties compared with 90 W and CNT groups. FT-NIR analysis was used to quantify water and sugar content of strawberries. As a result, it was concluded that ultrasound power as high as 90 W resulted in detrimental effects on strawberry quality, while power levels between 30 W and 60 W had improved quality and can be used to extend shelf life of strawberry.

Development of Novel Calibrations for FT-NIR Analysis of Protein, Oil, Carbohydrates and Isoflavones in Foods

AbstractThe development of calibration methodology for novel FT-NIRS analysis of soybean-based foods is presented together with high-precision NIRS spectra and composition measurements in terms of proteins, oil and carbohydrates in soybean-based foods/soy foods.

Effect of oxygen and carbon dioxide absorbers on strawberry quality

This study investigated the effect of active modified atmosphere packaging on quality of fresh strawberries. Strawberries were treated with one oxygen and two different carbon dioxide scavengers throughout storage at 4 °C for 4 weeks. The effect of active packaging was assessed by comparing gas concentrations, pH, electrical conductivity, total soluble solids, surface color, decay incidence, texture profile analysis, sensory analysis, and FT-NIR analysis values. pH values were significantly higher in the controls (package without absorbers) during storage compared with other treatments. The package headspace with CO 2 absorbers, had the lowest CO 2 accumulation and O 2 absorbers resulted in constant O 2 levels (5 kPa) during storage. Total soluble solid contents of all treatments were between 10.34% and 7.7% except for the control which had a value of 6.94% at the end of the storage. CO 2 absorbers are clearly effective for maintaining soluble solid contents. Electrical conductivity was lowest with CO 2 absorbers throughout storage and color was better maintained in all treated fruit. Firmness values of the controls were significantly lower than those of the treatments. Results of sensory evaluation showed that the controls had the lowest scores for all attributes. This study shows that the FT-NIR is a powerful-nondestructive tool for monitoring quality of strawberries and produces rapid readings during storage. In general, our study showed that the use of active packaging preserves strawberry.

Soil organic matter quality and microbial catabolic functions along a gradient of wildfire history in a Mediterranean ecosystem

The principal aim of this research was to determine the influence of an increasing wildfire history on the recovery at short and long term of soil organic matter (SOM) composition and microbial properties. The contemporary wildfire events (since 1950) were recorded for 27 plots located on the siliceous part of the French Mediterranean region (Maures mountain ranges). A wildfire history index was built, tested and calculated in order to display numerical values representative of the different wildfire history parameters (i.e. number of fires, time since fire and mean fire interval). Microbial basal respiration and biomass were analysed as well as intensity of the use of 31 C-substrates, catabolic diversity and C-substrates utilisation profiles. Furthermore, a qualitative characterisation of the SOM was carried out by solid state 13C NMR. Potential drivers of the microbial recovery were identified by studying the relationships between microbial activities and chemical functions of SOM. Our results showed that fire histories resulting in considerable losses or alterations of SOM, such as recent or close fires, decreased the microbial catabolic evenness. This could be attributed to a preferential utilisation of N-containing compounds and complex substrates such as aromatic and polymers reflecting a greater N microbial demand and a selection of specific catabolic functions. Moreover, a large number of fires (4 fires in 57 years compared to 1–2) resulted in lasting degradation of the relative intensity of methyl C function in polymethylene, O-Alkyl C, aromatic C and phenolic C functions inducing a slow-down in recovery of microbial properties. These results also confirm our hypothesis that some chemical functions of SOM can be in equilibrium with wildfire history. Finally, this research demonstrates that FT-NIR analysis can be used as a valuable tool to assess both the wildfire history and the vulnerability of soil quality to shifts in historical fire regimes.

Application of FT NIR Spectroscopy in the Determination of Basic Physical and Chemical Properties of Sausages

The objectives of this study were to develop calibration models for determination of water activity and the content of fat, dry matter, salt, non collagen muscle protein and pH in dry cooked sausages. Samples (n = 42) were scanned in FT-NIR Analyzer and simultaneously analyzed by standard methods. The spectra were measured in the reflectance mode with a compressive cell between 10 000 and 4 000 cm-1, averaging 100 scans. Calibration models were developed using the partial least squares (PLS) method. These calibration models were checked later by crossvalidation. The following statistical values were obtained: R (correlation coefficient) = 0.997 and SEC (standard error of calibration) = 0.002 for water activity, R = 0.966 and SEC = 0.023 for pH, R = 0.995 and SEC = 0.970 for dry matter content, R = 0.995 and SEC = 0.045 for salt content, R = 0.965 and SEC = 0.652 for non collagen muscle protein, R = 0.996 and SEC = 0.559 for fat content. The results of the study showed that FT-NIR is a suitable method for rapid analysis of physical and chemical properties of sausages.

FT-IR microimaging spectroscopy: A comparison between healthy and neoplastic human colon tissues

FT-IR microimaging was performed on colon tissues with the aim to characterize spectral ‘markers’ to distinguish healthy from pathological tissues. Evidences of spectral peculiarities were mainly found in the finger print region even in the presence of a low grade adenocarcinoma. The occurrence of inflammation and necrotic states can also be demonstrated. Through statistical analysis as well as custom map procedures it was possible to reconstruct the topological distribution of different biochemical states and to verify results from the histopathological analysis. Preliminary results from FT-NIR analysis are in substantial agreement with those in the mid infrared region.

Study of the effect of lamination process on pasta by physical chemical determination and near infrared spectroscopy analysis

The objective of this study was to evaluate the chemical and physical characteristics of fresh egg pasta samples obtained by using two different production methodologies: extrusion and lamination. The results of the study demonstrate that products have different characteristics in terms of colour and level of gelatinization, while they do not show significant differences with regard to the amount of absorbed water during the cooking stage. It was evaluated that it is possible to discriminate the two kinds of products by using FT-NIR spectroscopy. FT-NIR analysis results suggest the presence of a different matrix–water association, a diverse level of starch gelatinization and a distinct starch–gluten interaction in the two kinds of pasteurised samples.