Fiber Optic NIR Research Articles

Discrimination between arabica and robusta coffees using NIR-integrating sphere spectroscopy coupled with hierarchical clustering analysis

In Indonesia, coffee farmers preferred to produce arabica and robusta coffee. Regarding its superior quality and commercial values, now the demand for specialty arabica and fine robusta coffee is increasing. In this research, discrimination between the two coffees was evaluated using NIR-integrating sphere spectroscopy coupled with the hierarchical clustering analysis (HCA) method. NIR spectral data in the region of 1175-1650 nm was measured using a portable fiber optic NIR spectrometer equipped with an integrating sphere from Ocean Optics (NIR-Quest, Ocean Optics, USA). Arabica (n=10) and robusta (n=10) ground roasted coffee (with mesh 50) was prepared as samples. The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were utilized in data analysis to discriminate between the specialty arabica and fine robusta coffee samples. The PCA and HCA results confirmed the good separation between the two coffees with arabica and robusta coffee samples were grouped in two distinct clusters. This result reveals that NIR-integrating sphere spectroscopy seems to be a potential analytical method dedicated to the discrimination of arabica and robusta coffee with minimum sample preparation.

Evaluation of intra-ring wood density profiles using NIRS: comparison with the X-ray method

Pith-to-bark wood density profiling is interesting in forestry science. By comparing it with the X-ray method, this study proved that a fiber optic NIR spectrometer with a high-precision displacement system could accurately measure intra-ring wood density with a spatial resolution of 0.5 mm. Most near-infrared spectroscopy (NIRS) studies for wood density determination use samples that have been pulverized beforehand. Attenuation of ionizing radiation is still the standard method to determine wood density with high spatial resolution. However, there is evidence that NIRS could be an accurate and affordable method for determining intra-ring density in solid wood strips. In this study, we research whether the results published for intra-ring density predictions in wood can be improved when calibrated with X-ray microdensitometry. The measurements were made using a fiber optic probe with a separation between measurement points of 0.508 mm in a range between 1200 and 2200 nm. A total of 4520 density points were used to create partial least squares regression (PLSR). X-ray densitometry data were used as reference values. Twenty PLSR calibrations were randomly executed on 31 samples collected from 28 Pinus radiata D. Don trees. Upon selecting 20 latent variables, the R 2 value was 0.873 for the training group and 0.895 for the validation group, while RMSEP values are 43.1 × 10−3 and 47.1 × 10−3 g cm−3 for the training and validation groups, respectively. The range error ratio (RER) was 13.7. The RER was high and almost in the range suggested for quantification purposes. Results are superior to wood density studies in the literature which do not employ spatial resolution and to those found in studies using hyperspectral imaging.

Development of a Portable Field Imaging Spectrometer: Application for the Identification of Sun-Dried and Sulfur-Fumigated Chinese Herbals.

We fabricated a visible-near-infrared (Vis-NIR) portable field imaging spectrometer with a prism-grating-prism element and a scanning mirror. The developed Vis-NIR imaging spectrometer, consisting of an INFINITY 3-1 detector and a V10E spectrometer from Specim Corporation, is designed to measure the spectral range between 0.4 and 1 µm with spectral resolution of 2-4 nm. In recent years, sulfur fumigation has been abused during the processing of certain freshly harvested Chinese herbs. Fourier transform infrared spectroscopy, fiber optic NIR spectrometry, and liquid chromatography-mass spectrometry are typically used to analyze the chemical profiles of sulfur-fumigated and sun-dried Chinese herbs. Field imaging spectrometry is rarely used to identify sulfur-fumigated herbs. In this study, field imaging spectrometry, principal component analysis, and the partial least squares-discriminant analysis multivariate data analysis method are used to distinguish sun-dried and sulfur-fumigated Chinese medicinal herbs with a sensitivity of 96.4% and a specificity of 98.3% for RPA identification. These results suggest that hyperspectral imaging is a potential technique to control medicine quality for medical applications.

FEASIBILITY OF USING A MINIATURE FIBER OPTIC UV‐VIS‐NIR SPECTROMETER TO ASSESS TOTAL POLYPHENOL INDEX, COLOR INTENSITY AND VOLUMIC MASS IN RED WINE FERMENTATIONS

ABSTRACT This study sought to assess the feasibility of using ultraviolet‐visible‐near infrared (UV‐VIS‐NIR) spectroscopy to monitor changes in total polyphenol index, color intensity and volumic mass, three indicators of quality during red wine fermentation. Samples (n = 68) collected from eleven tanks during fermentation were scanned in three types of quartz flow cells, path lengths 0.1, 2 and 50 mm, in the UV‐VIS‐NIR region (200–1,100 nm), using a fiber spectrometer system in transmission mode. Principal component analysis and partial least squares regression were used to interpret spectra and develop calibrations for predicting wine composition during fermentation. Models for the prediction of total polyphenol index displayed coefficients of determination (r2) ranging between 0.21–0.98, whereas values for the standard error of cross‐validation (SECV) ranged from 2.29 to 14.91, depending on the spectral region used. Values for the prediction of color intensity were: r2 = 0.56–0.98 and SECV = 0.43–1.88. Corresponding values for volumic mass were r2 = 0.31–0.94 and SECV = 8.71–30.20 g/dm3. These results suggest that UV‐VIS‐NIR spectroscopy using a miniature fiber optic spectrometer as a promising tool could be used as an alternative method for the rapid monitoring of quality parameters during red wine fermentation.PRACTICAL APPLICATIONSThe study develops ultraviolet‐visible‐near infrared calibrations to monitor changes in total polyphenol index, color intensity and volumic mass, main indicators of quality during red‐wine fermentation, taking into account and responding to the requirements of the enological industry that looks for a swift, efficient, economical and – particularly – nondestructive analytical technique for measuring quality parameters during fermentation process with a single instrument and in a matter of seconds, saving time and enhancing the decision‐making process. The work also evaluates the efficacy of a simple, efficient and low‐cost instrument that belongs to so called new near infrared spectroscopy (NIRS) generation, which are most suitable to be implemented for on‐line analysis in the winemaking industry. The results suggest that the miniature fiber optic NIR spectrometer is a promising tool for monitoring the red wine fermentation process, allowing the instantaneous prediction of the main parameters to be controlled. At the same time, suitable wavelengths for these parameters are also proposed according to x‐loading weights and regression coefficients.

Reflection–absorption-based near infrared spectroscopy for predicting water content in bio-oil

A novel fiber-optic NIR system based on reflection–absorption phenomenon for predicting water content in bio-oil was developed. System successfully overcomes from the sampling limitations posed by dark and sticky nature of bio-oil. Spectral information in near infrared region (900–2100 nm) was obtained for bio-oils with varied levels of water content. The feasibility of designing an NIR system for estimating the water content in bio-oil was tested by applying multivariate analysis to the spectral data. Two important techniques of multivariate data analysis, principal component regression (PCR) and partial least squares (PLS) were independently applied to experimental results to construct theoretical models. The calibration results demonstrated that spectral information can successfully predict the bio-oil water content (from 16% to 36%) with less than 2% of error and with a high coefficient of determination ( R 2 > 0.85). The reliability of a fiber-optic NIR system for predicting the bio-oil water content indicates that there is potential for utilization of NIR spectroscopy in monitoring bio-oil quality during production.

A feasibility study on the use of a miniature fiber optic NIR spectrometer for the prediction of volumic mass and reducing sugars in white wine fermentations

NIR spectroscopy was used to assess two physical/chemical parameters in white wines during alcoholic fermentation. NIR calibration models were developed using a set of 64 samples scanned in a rectangular quartz cuvette with a 50 mm path-length in the 700–1060 nm region, in a miniature fiber optic NIR spectrometer system working in transmission mode. Principal component analysis (PCA) and partial least squares (PLS) regressions were used to interpret spectra and develop calibrations for wine composition. The correlation coefficient ( r) and the standard error of cross-validation (SECV) were 0.99, 4.22 g/dm 3 for volumic mass and 0.99, 10.44 g/l, for reducing sugars, respectively. Suitable wavelengths for volumic mass and reducing sugars were also proposed according to x-loading weights and regression coefficients. The results obtained suggest that the miniature fiber optic NIR spectrometer is a promising tool for monitoring the white wine fermentation process.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1100 to 2500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient ( R 2). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R 2 value of 0.996–0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Monitoring of technical oils in supercritical CO 2 under continuous flow conditions by NIR spectroscopy and multivariate calibration

Metal parts and residues from machining processes are usually polluted with cutting or grinding oil and have to be cleaned before further use. Supercritical carbon dioxide can be used for extraction processes and precision cleaning of metal parts, as developed at Forschungszentrum Karlsruhe. For optimizing and efficiently conducting the extraction process, in-line analysis of oil concentration is desirable. Therefore, a monitoring method using fiber-optic NIR spectroscopy in combination with PLS calibration has been developed. In an earlier paper we have described the instrumental set-up and a calibration model using the model compound squalane in the spectral range of the CH combination bands from 4900 to 4200 cm −1. With this model only poor prediction results were obtained if applied to technical oil samples in supercritical CO 2. In this paper we describe a new calibration model, which was set up for the squalane/carbon dioxide system covering the 323–353 K temperature and the 16–35.6 MPa pressure range. Here, calibration data in the spectral range from 6100 to 5030 cm −1 have been used. This range includes the 5100 cm −1 CO 2 band of the Fermi triad as well as the hydrocarbon 1st overtone C H stretching bands, where spectral features of oil compounds and squalane are more similar to each other. The root mean-squared error of prediction obtained with this model is 4 mg cm −3 for carbon dioxide and 0.4 mg cm −3 for squalane, respectively. The utilizability of the newly developed PLS calibration model for predicting the oil concentration and CO 2 density of solutions of technical oils in supercritical carbon dioxide has been tested. Three types of “real world” cutting and grinding oil formulations were used in these experiments. The calibration proved to be suitable for determining the technical oil concentration with an error of 1.1 mg cm −3 and the CO 2 density with an error of 6 mg cm −3. Therefore, it seems possible to apply this in-line analytical approach on the basis of a cost-effective and time-saving model compound calibration for the surveillance of real world de-oiling and other extraction process based on supercritical carbon dioxide, and furthermore to establish an automated process termination criterion based on this technique.

Fiber optic near-infrared Raman spectroscopy for clinical noninvasive determination of water content in diseased skin and assessment of cutaneous edema

Currently, measuring Raman spectra of tissues of living patients online and in real time, collecting the spectra in a very short measurement time, and allowing diagnosis immediately after the spectrum is recorded from any body region, are specific advantages that fiber optic near-infrared Raman spectroscopy (NIR RS) might represent for in vivo clinical applications in dermatology. We discuss various methodological aspects and state of the art of fiber optic NIR RS in clinical and experimental dermatology to outline its present advantages and disadvantages for measuring skin in vivo, particularly its water content. Fiber optic NIR Fourier transform (FT) RS has been introduced to dermatological diagnostics to obtain information regarding the molecular composition of the skin up to several hundred micrometers below the skin surface in a relatively fast nondestructive manner. This has been especially important for probing for in vivo assessment of cutaneous (intradermal) edema in patients patch test reactions. Fiber optic NIR FT Raman spectrometers still require further technological developments and optimization, extremely accurate water concentration determination and its intensity calculation in skin tissue, and for clinical applications, a reduction of measurement time and their size. Another promising option could be the possibility of applying mobile and compact fiber optic charge-coupled device (CCD)-based equipment in clinical dermatology.

Feasibility and Performances of a New, Multiplexed, Fast and Low-Cost Fiber-Optic NIR Spectrometer for the On-Line Measurement of Sugar in Fruits

This paper describes an attempt to develop a new NIR spectrometer to sort fruits according to the sugar content in the conditioning stations. This spectrometer, ranging from 800 to 1050 nm, is based on diode array technology. It is coupled with fiber optics, which allow multiplexing; 20 fibers have been detected by the camera, showing the potential multiplexing of 20 different points. The acquisition time is 40 ms per image (i.e., 2 ms per spectrum). A good signal-to-noise ratio has been recorded in the 820–1010 nm range. This spectrometer has been tested with a set of 79 peaches. Partial least-squares processing is applied to different wavelength ranges. The best coefficient of correlation, achieved with a reduced wavelength range (from 847 to 977 nm), is 0.81, and the standard error of prediction is 1.04°Brix. With this calibration, the fruits can be sorted into three maturity classes with a reliability of 76%. This performance, as well as the other characteristics of the instrument, is in accordance with the requirements of the conditioning station managers.