Near-infrared Instrument Research Articles

Searches for Population III pair-instability supernovae: Predictions for ULTIMATE-Subaru and WFIRST

Abstract ULTIMATE-Subaru (Ultra-wide Laser Tomographic Imager and MOS with AO for Transcendent Exploration on Subaru) and WFIRST (Wide Field Infra-Red Survey Telescope) are the next generation of near-infrared instruments that have a large field-of-view. They allow us to conduct deep and wide transient surveys in the near-infrared. Such a near-infrared transient survey enables us to find very distant supernovae that are redshifted to the near-infrared wavelengths. We have performed mock transient surveys with ULTIMATE-Subaru and WFIRST to investigate their ability to discover Population III pair-instability supernovae. We found that a five-year 1 deg2K-band transient survey with a point-source limiting magnitude of 26.5 mag with ULTIMATE-Subaru may find about two Population III pair-instability supernovae beyond the redshift of 6. A five-year 10 deg2 survey with WFIRST reaching 26.5 mag in the F184 band may find about seven Population III pair-instability supernovae beyond the redshift of 6. We also find that the expected numbers of the Population III pair-instability supernova detections increase by about a factor of 2 if the near-infrared transient surveys are performed towards clusters of galaxies. Other supernovae, such as Population II pair-instability supernovae, would also be detected in the same survey. This study demonstrates that these future wide-field near-infrared instruments allow us to investigate the explosions of first-generation supernovae by performing deep and wide near-infrared transient surveys.

NIR and MIR spectral data fusion for rapid detection of Lonicera japonica and Artemisia annua by liquid extraction process

Lonicera japonica and Artemisia annua are two widely used plants that are typically combined together as botanical dietary supplements or herbal medicines. In this study, the liquid extraction process of various mixtures of these two plants was investigated with near-infrared (NIR) and mid-infrared (MIR) spectroscopy for rapid detection of important effective compounds. Eight main effective compounds of Lonicera japonica and Artemisia annua mixtures were modeled and selected as quality attributes: neochlorogenic acid (5-CQA), cryptochlorogenic acid (4-CQA), chlorogenic acid (3-CQA), caffeic acid (CA), isochlorogenic acid B (ICAB), isocholorogenic acid A (ICAA), isocholorogenic acid C (ICAC), and secoxyloganin (SL). First, partial least squares (PLS) was adopted to build quantitative models by combining spectra from MIR and NIR instruments. Two multiblock PLS methods were applied using NIR and MIR data matrix in which concatenated PLS (C-PLS) and sequential and orthogonalized PLS (SO-PLS) were utilized to optimize model performance. The results showed that models based on single NIR and MIR spectroscopy had close capacity for detection of the eight indexes. However, performance of the fusion methods C-PLS and SO-PLS both achieved better results than the results of single NIR or MIR PLS models, with SO-PLS being superior to C-PLS. This indicates that the multiblock methodology can be used to optimize rapid detection of indexes from complex industrial processes of plants using NIR and MIR spectroscopy.

Discrimination of Camellia japonica cultivars and chemometric models: An interlaboratory study

Camellia japonica is a valued plant since ancient times throughout the world mostly due to their ornamental flowers. It has a high number of cultivars, with very similar phenotypic and genotypic characteristics which are difficult to discriminate, being some of them often rare and with a high price at the market. Their discrimination is mostly done through visual inspection of the morphologic characteristics which is a hard and inefficient task. Spectroscopic techniques had already been used for taxonomic purposes at species and sub-species level with success and could be an alternative for accurate C. japonica cultivars discrimination. Despite the already recognized success of such techniques, most of the studies arises from a single laboratory and little is known about the robustness of these techniques regarding interlaboratory data transferability. In this context, the work developed herein presents a double aim: (I) to explore the ability of near infrared (NIR) spectroscopy and partial least square – discriminant analysis (PLS-DA) to discriminate C. japonica cultivars and (II) to evaluate data transferability between two independent laboratories (Lab A and Lab B). Air-dried leaves NIR spectra of 43 C. japonica plants (15 distinct cultivars) were acquired in both laboratories using two similar NIR instruments (same manufacturer and model). Spectra were further modelled by PLS-DA after exploratory analysis using principal component analysis (PCA): (I) individually for Lab A and Lab B; (II) using Lab A as calibration and Lab B as validation set and vice-versa and (III) with using Lab A and Lab B data together. The percentage of C. japonica cultivars discrimination for both laboratories was nearly the same (around 83%) indicating no significant differences between Lab A and Lab B analysis. However, the results were quite poor when spectra were modelled with data from a single laboratory and validated with the other (65.5 and 63.8%). When data were merged, 85.9% of correct cultivars assignments were obtained. The results herein obtained could benefit of including additional cultivars and plants; but demonstrated the ability of NIR spectroscopy for C. japonica cultivars discrimination. Regarding data transferability, even when dealing with a similar instrument, some issues arisen preventing easy and efficient spectral library transfers.

Joint and Unique Multiblock Analysis for Integration and Calibration Transfer of NIR Instruments.

In the present paper, we introduce an end-to-end workflow called joint and unique multiblock analysis (JUMBA), which allows multiple sources of data to be analyzed simultaneously to better understand how they complement each other. In near-infrared (NIR) spectroscopy, calibration models between NIR spectra and responses are used to replace wet-chemistry methods, and the models tend to be instrument-specific. Calibration-transfer techniques are used for standardization of NIR-instrumentation, enabling the use of one model on several instruments. The current paper investigates both the similarities and differences among a variety of NIR instruments using JUMBA. We demonstrate JUMBA on both a previously unpublished data set in which five NIR instruments measured mushroom substrate and a publicly available data set measured on corn samples. We found that NIR spectra from different instrumentation largely shared the same underlying structures, an insight we took advantage of to perform calibration transfer. The proposed JUMBA transfer displayed excellent calibration-transfer performance across the two analyzed data sets and outperformed existing methods in terms of both prediction accuracy and stability. When applied to a multi-instrument environment, JUMBA transfer can integrate all instruments in the same model and will ensure higher consistency among them compared with existing calibration-transfer methods.

Determining damage levels in wheat caused by Sunn pest (Eurygaster integriceps) using visible and near-infrared spectroscopy

A commercial near-infrared (NIR) instrument for bulk samples and a modified Single Kernel Near-Infrared (SKNIR) instrument equipped with a visible or NIR spectrometer were studied as a way to measure damage levels caused by Sunn pest (SP) in wheat. Sunn pest causes damage by feeding on wheat berries and injecting a salivary enzyme damaging the gluten. For measurement of SP damage in bulk wheat, NIR calibration models developed for mixtures containing 0–10% and 0–100% sound and SP damaged wheat resulted in R2 of 0.25 and 0.89 and SECVs of 2.75 and 10.9, respectively. The 0–100% model was considered a qualitative measure of damage, but predictions were poor over 0–10% which is a critical range for commercial applications. Discrimination between single kernels of Sound and SP-damaged was typically good, with classification accuracy averaging ∼75% for both visible and NIR although some were poor, which greatly affected the average. Average classification accuracy was ∼85% for spectral data that contained kernels from all samples. While the potential for using visible or NIR spectroscopy was shown, results highlighted the need to develop a more robust SP classification model to further evaluate the single kernel model.

Short Communication: The potential of portable near infrared spectroscopy for assuring quality and authenticity in the food chain, using Iberian hams as an example

This communication assesses the use of a portable near infrared (NIR) instrument to measure quantitative (fatty acid profile) properties and qualitative (‘Premium’ and ‘Non-premium’) categories of individual Iberian pork carcasses at the slaughterhouse. Acorn-fed Iberian pigs have more unsaturated fats than pigs fed conventional compound feed. Recent advances in miniaturisation have led to a number of handheld NIR devices being developed, allowing processing decisions to be made earlier, significantly reducing time and costs. The most common methods used for assessing quality and authenticity of Iberian hams are analysis of the fatty acid composition of subcutaneous fat using gas chromatography and DNA analysis. In this study, NIR calibrations for fatty acids and classification as premium or non-premium ham, based on carcass fat measured in situ, were developed using a portable NIR spectrometer. The accuracy of the quantitative equations was evaluated through the standard error of cross validation or standard error of prediction of 0.84 for palmitic acid (C16:0), 0.94 for stearic acid (C18:0), 1.47 for oleic acid (C18:1) and 0.58 for linoleic acid (C18:2). Qualitative calibrations provided acceptable results, with up to 98% of samples (n = 234) correctly classified with probabilities ⩾0.9. Results indicated a portable NIR instrument has the potential to be used to measure quality and authenticity of Iberian pork carcasses.

Rapid and Automatic Classification of Tobacco Leaves Using a Hand-Held DLP‑Based NIR Spectroscopy Device

A hand-held near infrared (NIR) spectroscopy device is much more convenient than a traditional desktop NIR instrument. Thus, it is more suitable for the practical application. An automatic and rapid tool for grading tobacco leaves on the spot using a hand-held digital light processing (DLP)-based NIR spectroscopy device is proposed in this paper. Firstly, the spectral data of the samples is scanned with a hand-held NIR device directly from the tobacco leaves without any samples preparation procedures. Then, the training model of different classes is built and the class of each test sample is predicted by using sparse representation classification (SRC) algorithm. Comparing with the traditional linear discriminant analysis (LDA) and support vector machine (SVM) algorithms, the classification accuracy of SRC method is the highest and has the least computation time. The results show that hand-held NIR spectroscopy technology could be a novel classification tool for grading tobacco leaves in the purchasing on the spot.

Compositional distributions and evolutionary processes for the near-Earth object population: Results from the MIT-Hawaii Near-Earth Object Spectroscopic Survey (MITHNEOS)

Advancing technology in near-infrared instrumentation and dedicated planetary telescope facilities have enabled nearly two decades of reconnoitering the spectral properties for near-Earth objects (NEOs). We report measured spectral properties for more than 1000 NEOs, representing >5% of the currently discovered population. Thermal flux detected below 2.5 µm allows us to make albedo estimates for nearly 50 objects, including two comets. Additional spectral data are reported for more than 350 Mars-crossing asteroids. Most of these measurements were achieved through a collaboration between researchers at the Massachusetts Institute of Technology and the University of Hawaii, with full cooperation of the NASA Infrared Telescope Facility (IRTF) on Mauna Kea. We call this project the MIT-Hawaii Near-Earth Object Spectroscopic Survey (MITHNEOS; myth-neos). While MITHNEOS has continuously released all spectral data for immediate use by the scientific community, our objectives for this paper are to: (1) detail the methods and limits of the survey data, (2) formally present a compilation of results including their taxonomic classification within a single internally consistent framework, (3) perform a preliminary analysis on the overall population characteristics with a concentration toward deducing key physical processes and identifying their source region for escaping the main belt. Augmenting our newly published measurements are the previously published results from the broad NEO community, including many results graciously shared by colleagues prior to formal publication. With this collective data set, we find the near-Earth population matches the diversity of the main-belt, with all main-belt taxonomic classes represented in our sample. Potentially hazardous asteroids (PHAs) as well as the subset of mission accessible asteroids (ΔV ≤ 7 km/s) both appear to be a representative mix of the overall NEO population, consistent with strong dynamical mixing for the population that interacts most closely with Earth. Mars crossers, however, are less diverse and appear to more closely match the inner belt population from where they have more recently diffused. The fractional distributions of major taxonomic classes (60% S, 20% C, 20% other) appear remarkably constant over two orders of magnitude in size (10 km to 100 m), which is eight orders of magnitude in mass, though we note unaccounted bias effects enter into our statistics below about 500 m. Given the range of surface ages, including possible refreshment by planetary encounters, we are able to identify a very specific space weathering vector tracing the transition from Q- to Sq- to S-types that follows the natural dispersion for asteroid spectra mapped into principal component space. We also are able to interpret a shock darkening vector that may account for some objects having featureless spectra. Space weathering effects for C-types are complex; these results are described separately by Lantz, Binzel, DeMeo. (2018, Icarus 302, 10–17). Independent correlation of dynamical models with taxonomic classes map the escape zones for NEOs to main-belt regions consistent with well established heliocentric compositional gradients. We push beyond taxonomy to interpret our visible plus near-infrared spectra in terms of the olivine and pyroxene mineralogy consistent with the H, L, and LL classes of ordinary chondrites meteorites. Correlating meteorite interpretations with dynamical escape region models shows a preference for LL chondrites to arrive from the ν6 resonance and H chondrites to have a preferential signature from the mid-belt region (3:1 resonance). L chondrites show some preference toward the outer belt, but not at a significant level. We define a Space Weathering Parameter as a continuous variable and find evidence for step-wise changes in space weathering properties across different planet crossing zones in the inner solar system. Overall we hypothesize the relative roles of planetary encounters, YORP spin-up, and thermal cycling across the inner solar system.

Portable Spectroscopy.

Until very recently, handheld spectrometers were the domain of major analytical and security instrument companies, with turnkey analyzers using spectroscopic techniques from X-ray fluorescence (XRF) for elemental analysis (metals), to Raman, mid-infrared, and near-infrared (NIR) for molecular analysis (mostly organics). However, the past few years have seen rapid changes in this landscape with the introduction of handheld laser-induced breakdown spectroscopy (LIBS), smartphone spectroscopy focusing on medical diagnostics for low-resource areas, commercial engines that a variety of companies can build up into products, hyphenated or dual technology instruments, low-cost visible-shortwave NIR instruments selling directly to the public, and, most recently, portable hyperspectral imaging instruments. Successful handheld instruments are designed to give answers to non-scientist operators; therefore, their developers have put extensive resources into reliable identification algorithms, spectroscopic libraries or databases, and qualitative and quantitative calibrations. As spectroscopic instruments become smaller and lower cost, "engines" have emerged, leading to the possibility of being incorporated in consumer devices and smart appliances, part of the Internet of Things (IOT). This review outlines the technologies used in portable spectroscopy, discusses their applications, both qualitative and quantitative, and how instrument developers and vendors have approached giving actionable answers to non-scientists. It outlines concerns on crowdsourced data, especially for heterogeneous samples, and finally looks towards the future in areas like IOT, emerging technologies for instruments, and portable hyphenated and hyperspectral instruments.

Hand-held near-infrared spectrometers: State-of-the-art instrumentation and practical applications

Recently, miniaturization of Raman, mid-infrared and near-infrared spectrometers has made substantial progress, and marketing companies predict for this segment of instrumentation a significant growth rate within the next few years. This increase will be based on a more frequent implementation for industrial quality and process control and a wider adoption of spectrometers for in-the-field testing and onsite measurements. However, contrary to Raman and MIR spectroscopy, miniaturization has been much further driven for near-infrared instrumentation. Thus, the present communication will focus on hand-held near-infrared spectrometers only and discuss their applications to selected analytical problems.

Screening wavelengths with consistent and stable signals to realize calibration model transfer of near infrared spectra.

Measurement environmental changes and spectral signal differences among multi-spectrometers may lead to big error in the process of transferring the model of near infrared (NIR) spectra to secondary instruments. Basing on the common sense, NIR calibration models could be shared directily among multi-instruments, if they were built on the wavelengths at which the spectral signals of secondary instruments are stable and well consistent with the primary's. Present work advanced a method named as screening wavelengths with consistent and stable signals (SWCSS) to transfer NIR calibration models. It eliminates the wavelengths at which the standard deviation of difference spectra between the primary and secondary instruments (SDDSI) is much higher than the standard deviation of precision detection spectra (SDPDS) of a sample tested on the primary and the wavelengths with higher SDPDS values. So that the spectral signals of different instruments at these selected wavelengths are consistent well and stable. The NIR calibration model is built by partial least square regression (PLS) based on the screened wavelengths. Two datasets of corn and radix scutellariae samples measured with different NIR instruments are used to test the performance of the method. The results show that the overall prediction performance of the SWCSS-PLS models for samples measured on secondaries is much better than that of the full-wavelength PLS models. The root mean square of error prediction (RMSEP) of the SWCSS-PLS models for samples tested on secondaries is equivalent or superior to that of the piecewise direct standardization (PDS) correction. The SWCSS-PLS model is of fewer parameters, robust and can give good prediction results for samples of secondary instruments. Because standard samples are no longer needed during transferring the calibration models to secondary instruments, SWCSS-PLS models could be directly shared by multi-spectroscopy instruments.

Flame : A flexible data reduction pipeline for near-infrared and optical spectroscopy

We present flame, a pipeline for reducing spectroscopic observations obtained with multi-slit near-infrared and optical instruments. Because of its flexible design, flame can be easily applied to data obtained with a wide variety of spectrographs. The flexibility is due to a modular architecture, which allows changes and customizations to the pipeline, and relegates the instrument-specific parts to a single module. At the core of the data reduction is the transformation from observed pixel coordinates (x, y) to rectified coordinates (lambda, gamma). This transformation consists in the polynomial functions lambda(x,y) and gamma(x,y) that are derived from arc or sky emission lines and slit edge tracing, respectively. The use of 2D transformations allows one to wavelength calibrate and rectify the data using just one interpolation step. Furthermore, the gamma(x,y) transformation includes also the spatial misalignment between frames, which can be measured from a reference star observed simultaneously with the science targets. The misalignment can then be fully corrected during the rectification, without having to further resample the data. Sky subtraction can be performed via nodding and/or modeling of the sky spectrum; the combination of the two methods typically yields the best results. We illustrate the pipeline by showing examples of data reduction for a near-infrared instrument (LUCI at the Large Binocular Telescope) and an optical one (LRIS at the Keck telescope).

Application of OPT101 sensor in ICU-specific near-infrared spectroscopy instrumentation

The OPT101 photoelectric sensor has the characteristics of good photoelectric response, built-in transimpedance amplifier and small size, which meet the needs of accurately near-infrared spectral measurement in medical near-infrared spectroscopy (NIRS) range. Our research team has developed a series of portable NIRS instruments for NIRS measuring in intensive care units (ICU). The characteristics and advantages of OPT101 in the development of clinical ICU-specific NIRS instruments were introduced. The research progress of our team on the development of ICU-NIRS instruments using OPT101 was reviewed. The prospect of OPT101 in clinical noninvasive detection was discussed. Key words: Optoelectronic sensor; Near-infrared spectroscopy; Thrombus diagnosis; Shock monitoring; Fatigue evaluation

Atomistic tight-binding computations of the structural and optical properties of CdTe/CdX (X=S and Se)/ZnS core/shell/shell nanocrystals

A study of CdTe/CdX (X=S and Se)/ZnS core/shell/shell nanocrystals is carried out using atomistic tight-binding theory and the configuration interaction method to provide information for applications in bioimaging, biolabeling, display devices and near-infrared electronic instruments. The calculations yield the dependences of the internal and external passivated shells on the natural behaviours of CdTe/CdX (X=S and Se)/ZnS core/shell/shell nanocrystals. The reduction of the optical band gaps is observed with increasing numbers of monolayers in the external ZnS shell due to quantum confinement. Interestingly, the optical band gaps of CdTe/CdS/ZnS core/shell/shell nanocrystals are greater than those of CdTe/CdSe/ZnS core/shell/shell nanocrystals. In the presence of an external ZnS-coated shell, electron–hole wave function overlaps, oscillation strengths, ground-state exchange energies and Stokes shift are improved, whereas ground-state coulomb energies and fine-structure splitting are reduced. The oscillation strengths, Stokes shift and fine-structure splitting are reduced with the increase in external ZnS shell thickness. The oscillation strengths, Stokes shift and fine-structure splitting of CdTe/CdS/ZnS core/shell/shell nanocrystals are larger than those of CdTe/CdSe/ZnS core/shell/shell nanocrystals. Reduction of the atomistic electron–hole interactions is observed with increasing external ZnS shell size. The strong electron–hole interactions are more probed in CdTe/CdS/ZnS core/shell/shell nanocrystals than in CdTe/CdSe/ZnS core/shell/shell nanocrystals.

An H-band Vector Vortex Coronagraph for the Subaru Coronagraphic Extreme Adaptive Optics System

The vector vortex is a coronagraphic imaging mode of the recently commissioned Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) platform on the 8 m Subaru Telescope. This multi-purpose high-contrast visible and near-infrared (R- to K-band) instrument is not only intended to serve as a VLT-class “planet-imager” instrument in the northern hemisphere, but also to operate as a technology demonstration testbed ahead of the ELTs-era, with a particular emphasis on small inner-working angle (IWA) coronagraphic capabilities. The given priority to small-IWA imaging led to the early design choice to incorporate focal-plane phase-mask coronagraphs. In this context, a test H-band vector vortex liquid crystal polymer waveplate was provided to SCExAO, to allow a one-to-one comparison of different small-IWA techniques on the same telescope instrument, before considering further steps. Here we present a detailed overview of the vector vortex coronagraph, from its installation and performances on the SCExAO optical bench, to the on-sky results in the extreme AO regime, as of late 2016/early 2017. To this purpose, we also provide a few recent on-sky imaging examples, notably high-contrast ADI detection of the planetary-mass companion κ Andromedae b, with a signal-to-noise ratio above 100 reached in less than 10 mn exposure time.

Application of Design of Experiment for Detection of Meat Fraud with a Portable Near-Infrared Spectrometer

Background: Meat fraud generated a huge outrage amongst customers in 2013 in Europe due to the horsemeat scandal. Portable and hand-held optical near-infrared (NIR, 4,00012,500 cm-1/800-2,500 nm) spectroscopy sensors are traded as promising fast, non-invasive and easy analytical tools that might be applicable at any independent place of inspection. In order to embrace the on-going trend towards instrumental miniaturization, it was the aim of the present feasibility study to evaluate the application of Design of Experiment for frequently applied portable micro-electro-mechanical system (MEMS) based spectrometer by comparing its performance to a bench-top Fourier-Transform polarization near-infrared (FT-NIR) instrument. Methods: 63 samples of different meat types (beef: 9, chicken: 10, mutton: 10, turkey: 10, pork: 10, horse meat: 14) were measured in order to classify the meat-type using a portable micro-electromechanical system (MEMS) based spectrometer and a bench-top Fourier-Transform polarization nearinfrared (FT-NIR) instrument, in order to compare the performance of both systems. In a second step different meat types were minced together in order to investigate the level of adulteration which can be detected using MEMS and FT-NIR. Design of Experiment (DoE) was applied to enhance results. Results: The accuracy of MEMS versus FT-NIR for identifying whole / minced pieces of chicken, pork, turkey, beef and mutton meat (63 samples) against horse meat appeared to be 75.0-100.0% (MEMS) vs. 62.5%-100.0% (FT-NIR) for whole pieces and 75.0-100.0% (MEMS and FT-NIR) for minced meat. When mincing different types of meat together, a maximum of 4 and 1 factors were required for establishing a PLS-R model using again the spectra recorded with MEMS and FT-NIR, respectively. The resulting quality parameters for the MEMS device were: R2=0.06-0.62, Standard Error of Cross Valdiation (SECV)= 17.33-32.91, Ratio of Performance to Deviation (RPD) =0,54-1,70 and for the FTNIR system: R2=0.85-0.94, SECV=7.52-13.83%, RPD=2.2-5.7 (FT-NIR). The limit of detection was found at 10% for the MEMS and at 1% for the FT-NIR device. Conclusion: Meat classification can be performed using the bench-top FT-NIR as well as the hand-held MEMS-NIR. Mincing the meat samples does not necessarily improve classification accuracy as information about the surface structure is lost. NIRS prediction models for adulterations were established for the bench-top system. Prediction models for the hand-held device are inconclusive and have to be improved by a larger sample set and/or further progress in miniaturization technique. Low level adulteration (<10%) may also be predictable with NIRS, but continuative research is necessary. Keywords: Design of experiment, meat fraud, near-infrared, hand-held spectrometer, MEMS, polarization.

Rapid evaluation of moisture content in bamboo chips using diode array near infrared spectroscopy

Moisture in biomass plays important role during storage, combustion and pelletization. In order to measure moisture content in bamboo chips, two diode array near-infrared instruments, NIR-Gun (600-1100 nm at 2 nm intervals) and Micro-NIR (1150-2150 nm at 7 nm intervals), were used for scanning bamboo chips. Total number of samples used for developing model after removing outliers was 252. The circumference and moisture content of bamboos used were in the range between 16-39 cm and 39-86% wet basis (wb) respectively. Partial least squares regression technique was used to develop the model to predict the moisture content in bamboo chips. The R2, SECV, SEP, bias and RPD of optimum model of NIR-Gun were found to be 0.924, 2.871% wb, 2.385% wb, -0.250% wb and 3.656, while for Micro-NIR model the values were found to be 0.743, 4.349% wb, 4.499% wb, 0.026% wb and 1.972 respectively. In prediction of moisture content in bamboo chip, both models show the effect of different constituents of bamboo more than moisture. This study indicates that the results are suitable for screening the moisture content in bamboo chips. This would be helpful for process controlling using the moisture parameter during drying, pelletization and thermochemical conversion.

Technical note: Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness

The aim of the study was to evaluate the feasibility of near infrared (NIR) transmittance spectroscopy to predict cheese ripeness using the ratio of water-soluble nitrogen (WSN) to total nitrogen (TN) as an index of cheese maturity (WSN/TN). Fifty-two Protected Designation of Origin cow milk cheeses of 5 varieties (Asiago, Grana Padano, Montasio, Parmigiano Reggiano, and Piave) and different ripening times were available for laboratory and chemometric analyses. Reference measures of WSN and TN were matched with cheese spectral information obtained from ground samples by a NIR instrument that operated in transmittance mode for wavelengths from 850 to 1,050 nm. Prediction equations for WSN and TN were developed using (1) cross-validation on the whole data set and (2) external validation on a subset of the entire data. The WSN/TN was calculated as ratio of predicted WSN to predicted TN in cross-validation. The coefficients of determination for WSN and TN were >0.85 both in cross- and external validation. The high accuracy of the prediction equations for WSN and TN could facilitate implementation of NIR transmittance spectroscopy in the dairy industry to objectively, rapidly, and accurately monitor the ripeness of cheese through WSN/TN.

Impact of temperature and relative humidity on the near infrared spectroscopy measurements of cotton fiber micronaire

A key cotton fiber property is micronaire. Micronaire can impact the fiber’s quality, textile processing efficiency, and fabric dye consistency. Fiber micronaire is normally measured in a laboratory under tight standard temperature and relative humidity (RH) environmental conditions (21 ± 1℃, 65 ± 2% RH). Near infrared (NIR) measurements have been performed both inside and outside of the laboratory, but measurements outside the laboratory have at times demonstrated reduced predictive capability, possibly due to the lack of standard environmental conditions. A program was implemented to determine the impact of non-standard conditions of temperature T and relative humidity RH on NIR micronaire results for bench-top and portable NIR instruments. Non-standard T and RH resulted in varying fiber moisture, which impacted the NIR spectral response. The NIR micronaire results were impacted by the non-standard conditioning for all instruments, with the lower wavelength region (∼910–1680 nm) portable instrument impacted the most. The impacts and deviations were greater at high temperature/RH compared to low temperature/RH conditioning. These results provide a rationale for the deviations observed previously in NIR micronaire results for outside the laboratory micronaire measurements with portable NIR units.

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy.

Moisture content (MC) is one of the most important quality parameters of green coffee beans. Therefore, its fast and reliable measurement is necessary. This study evaluated the feasibility of near infrared (NIR) spectroscopy and chemometrics for rapid and non-destructive prediction of MC in intact green coffee beans of both Coffea arabica (Arabica) and Coffea canephora (Robusta) species. Diffuse reflectance (log 1/R) spectra of intact beans were acquired using a bench top Fourier transform NIR instrument. MC was determined gravimetrically according to The International Organization for Standardization (ISO) 6673. Samples were split into subsets for calibration (n = 64) and independent validation (n = 44). A three-component partial least squares regression (PLSR) model using raw NIR spectra yielded a root mean square error of prediction (RMSEP) of 0.80% MC; a four component PLSR model using scatter corrected spectra yielded a RMSEP of 0.57% MC. A simplified PLS model using seven selected wavelengths (1155, 1212, 1340, 1409, 1724, 1908, and 2249 nm) yielded a similar accuracy (RMSEP: 0.77% MC) which opens the possibility of creating cheaper NIR instruments. In conclusion, NIR diffuse reflectance spectroscopy appears to be suitable for rapid and reliable MC prediction in intact green coffee; no separate model for Arabica and Robusta species is needed.