Near-infrared Spectrometer Research Articles

A correction method for mitigating absorbance discrepancies between near-infrared spectrometers through the incorporation of blended carbon-titanium dioxide powder

In near-infrared spectroscopy analysis, ensuring the accurate transfer of models between different instruments relies on maintaining the accuracy of instrument wavelengths and absorbance. To mitigate absorbance drift at different wavelength points, this paper proposes a near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples. The method establishes a quadratic polynomial relationship model for the absorbance of each wavelength point between the main instrument and slave instruments. The study utilized two S450 grating-based diffuse reflection near-infrared spectroscopy instruments, with one serving as the main instrument and the other as the slave instrument. The point-by-point quadratic polynomial was employed to correct wheat spectra collected by the slave instruments, and a crude protein content prediction model for wheat was established, comparing it with linear regression correction. After correction, the average Euclidean distance of wheat spectra decreased by 66.71%, from 0.0937 to 0.0321, and the average peak-valley Euclidean distance decreased by 72.28%, from 0.0203 to 0.0056. The standard deviation of the predicted results decreased by 90.69%, from 1.4372 to 0.1338. The correction effect of the method combined with traditional preprocessing methods was superior to using preprocessing methods alone. Overall, the near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples significantly reduces spectral differences between different instruments, enhances spectral consistency, and diminishes prediction errors, achieving improved model sharing between instruments.

Sample data selection based on the feature subspace for photometric correction of the Chang'E-4 in situ observations

The lunar surface exhibits noticeable variations in photometric properties across different regions, influenced by their specific compositional and physical characteristics. These variations are particularly pronounced at the millimeter to centimeter scale. In this study, we propose a feature subspace-based sample selection method to identify Chang'E-4 Visible Near-infrared Imaging Spectrometer (CE-4 VNIS) in situ observations that share similar photometric properties at the centimeter scale. By constructing a feature subspace through principal component transformation, the CE-4 VNIS observation sites with comparable surface photometric properties are able to be selected from a series of observations. The selection results demonstrate that the majority of observation sites are covered by the lunar regolith, with a few exceptions consisting of rocks, soil bulk, shadows, and rough surfaces. These observations should be excluded from the photometric correction process during phase function fitting. Analyzing the photometric performance of the VNIS data with lunar regolith reveals that reflectance decreases as the phase angle increases below 80°, while above 80°, reflectance increases with increasing phase angle. This phenomenon is likely attributed to stronger forward scattering. Thus, a second-order polynomial is employed to fit the phase function for VNIS data. Consequently, a photometric model is developed for lunar regolith observed by CE-4 VNIS, incorporating the fitted phase function. Experimental results demonstrate reduced differences among observations with varying phase angles, and the corrected VNIS spectra exhibit a phase-reddening effect. These findings validate the effectiveness of the proposed model. A comparison between the corrected data with and without sample selection reveals a 33% decrease in mean standard deviation for VIS/NIR bands and a 19% decrease for SWIR bands in the corrected spectra after sample selection. These results indicate the potential applicability of our method for future VNIS observations, enabling the acquisition of photometrically consistent data.

Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed

This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral resolution and small sample sizes. The research concentrates on the near-infrared spectra of corn feed, utilizing spectral processing techniques and CNNs to precisely estimate crude protein content. Five preprocessing methods were implemented alongside two-dimensional (2D) correlation spectroscopy, resulting in the development of both one-dimensional (1D) and 2D regression models.A comparative analysis of these models in predicting crude protein content demonstrated that 1D-CNNs exhibited superior predictive performance within the 1D category. For the 2D models, CropNet and CropResNet were utilized, with CropResNet demonstrating more accurate and superior predictive capabilities. Overall, the integration of 2D correlation spectroscopy with suitable preprocessing techniques in deep learning models, particularly the 2D CropResNet, proved to be more precise in predicting the crude protein content in corn feed. This finding emphasis the potential of this approach in the portable spectrometer market.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

Non-destructive identification of wool and cashmere fibers based on improved LDA using NIR spectroscopy

Abstract With the advancement of near-infrared (NIR) spectroscopy and chemometrics technology, non-destructive qualitative testing has been widely applied in many fields. Both wool and cashmere are keratin protein fibers with many similarities in tissue structure, making it very difficult to distinguish between them. In order to achieve rapid and non-destructive identification of wool and cashmere, an improved linear discriminant analysis (ILDA) algorithm combined with NIR spectroscopy technology is proposed. The proposed method can also be used for the classification of extremely similar fibers and substances, with better classification performance. First, the spectral data of wool and cashmere are collected using an NIR spectrometer so as to reduce the influence of noise in the spectra; data preprocessing methods are used to correct the collected fiber spectra. Then, principal component analysis (PCA), linear discriminant analysis (LDA), and ILDA are used to extract the characteristic variables from the spectral data. Finally, the extracted characteristic variables are input into the machine learning algorithm K-nearest neighbor (K-NN) classifier. In the experimental stage, three dimensionality reduction methods (PCA, LDA, and ILDA) are evaluated using the K-NN classification model. The fiber classification accuracy can reach 97% when using the ILDA method for dimensionality reduction. The results show that the proposed method is effective for the qualitative detection of different types of wool and cashmere fibers.

Geographical origin discrimination of black beans using near-infrared (NIR) spectroscopy combined with fuzzy regularized complete linear discriminant analysis (FRCLDA)

Black beans from different geographical origins exhibit variations in terms of their nutritional and economic values. Therefore, ensuring traceability of the geographical origin of black beans is essential for both consumers and the product processing industries. The study designed a comprehensive black bean origin identification model using near-infrared (NIR) spectroscopy combined with discriminant analysis algorithms. To enhance the performance of the model, this study incorporated fuzzy logic into regularized complete linear discriminant analysis (RCLDA) and proposed the fuzzy regularized complete linear discriminant analysis (FRCLDA). This innovative approach aims to extract additional information from overlapping NIR spectra of black beans. NIR spectra of black beans from five locations were collected employing a portable near-infrared spectrometer. In the preprocessing stage, the Savitzky–Golay (SG) and mean centering (MC) were applied to process the raw spectra. Subsequently, complete linear discriminant analysis (CLDA), RCLDA, and FRCLDA were utilized to extract the information from the processed data. Finally, k-nearest neighbor (KNN), support vector machines (SVM), and extreme learning machine (ELM) were employed to classify the black bean samples based on the extracted features. The results indicated that KNN achieved the best classification performance. The FRCLDA-KNN model achieved the exceptional performance with a classification accuracy of 99.23% on the test set. Therefore, the system demonstrates satisfactory accuracy and performance in accurately identifying black beans from different geographical origins.

Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy

Microplastics are recognized as a new environmental pollutant. Researchers have detected their presence in waste incineration ash. However, traditional testing methods take a very long testing period. There is a lack of research on detecting microplastics in waste incineration ash. In this paper, a portable near-infrared spectra (NIRS) spectrometer was used for qualitative discrimination and quantitative prediction of microplastics in ash. A total of 84 sets of simulated ash samples containing different types (PP, PS, PE, and PVC) and contents (2.4 wt% − 20 wt%) of microplastics were used in the model. The results show the qualitative discrimination model using support vector machines (SVM) method with multiplicative scatter correction (MSC) preprocessing could effectively identify the microplastic types in the ash with 100% detection accuracy. Furthermore, the partial least squares regression (PLSR) model was effective in quantitatively predicting the content of microplastics in ash. The Rp2 of the PP, PS, PE, and PVC models are 0.95, 0.93, 0.89, and 0.95, respectively. The RPD of the PP, PS, PE, and PVC models are 3.97, 3.96, 2.89 and 5.02, respectively. This study shows that microplastics in ash can be detected rapidly and accurately using portable near-infrared spectrometers.

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS)

Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.

In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy

This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm-1. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer). III. Spectral calibration.

The Moons And Jupiter Imaging Spectrometer (MAJIS) is the visible and near-infrared imaging spectrometer onboard the European Space Agency (ESA)'s Jupiter Icy Moons Explorer mission. Before its integration into the spacecraft, the instrument undergoes an extensive ground calibration to establish its baseline performances. This process prepares the imaging spectrometer for flight operations by characterizing the behavior of the instrument under various operative conditions and uncovering instrumental distortions that may depend on instrumental commands. Two steps of the on-ground calibration campaigns were held at the instrument level to produce the data. Additional in-flight measurements have recently been obtained after launch during the Near-Earth Commissioning Phase. In this article, we present the analyses of these datasets, focusing on the characterization of the spectral performances. First, we describe and analyze the spectral calibration datasets obtained using both monochromatic sources and polychromatic sources coupled with solid and gas samples. Then, we derive the spectral sampling and the spectral response function over the entire field of view. These spectral characteristics are quantified for various operational parameters of MAJIS, such as temperature and spectral binning. The derived on-ground performances are then compared with in-flight measurements obtained after launch and presented in the framework of the MAJIS performance requirements.

In-situ detection on near-infrared spectra fingerprints of asphalt mixture after laboratory short- and long-term aging

Although Fourier transform infrared spectroscopy has been widely used in research on the aging of asphalt binders, it is a cumbersome, time-consuming, and environmentally polluting process using toxic solvents to extract and recover asphalt binders from asphalt mixtures for spectral measurement and analysis. However, little effort has been devoted towards its non-destructive spectral measurement, which is important for intelligent monitoring of pavement deterioration conditions. This paper focused on the non-destructive investigation of comparative near-infrared spectra of asphalt mixture specimens designed using the Marshall method after laboratory short- and long-term aging with a portable near-infrared spectrometer. The near-infrared spectra showed that asphalt mixtures from different aging states appeared significantly different fingerprints. The near-infrared fingerprints and aging states were also statistically significantly different according to the analysis of one-way analysis of variance. These distinctive near-infrared fingerprints could be attributed to the varied amounts of chemical functional groups, mainly hydrogen-containing groups during oxidative aging. This study provided a non-destructive and in-situ detection of the aging of asphalt mixtures.

Climatology of gravity wave activity based on two Martian years from ACS/TGO observations

Context. Gravity waves redistribute energy and momentum between the lower and upper atmosphere, thus providing vertical coupling between layers, and they affect the state, dynamics, and variability of the upper atmosphere. The statistics of gravity wave activity on Mars is poorly explored but is required in order to characterize the atmospheric circulation and to constrain numerical models. Aims. We present the gravity wave statistics accumulated over two Martian years: from the second half of Martian year 34 to the middle of Martian year 36 (May 2018 to February 2022). The statistics includes seasonal and latitude distributions of the wave potential energy and drag, serving to represent the wave activity and impact on the atmospheric dynamics. Methods. The observations were performed by the middle- and near-infrared spectrometers of the Atmospheric Chemistry Suite on board the ExoMars Trace Gas Orbiter. The temperature profiles we obtained independently from both channels during simultaneous measurements show a good agreement, thus providing verification and enhancing confidence in the data. The gravity wave parameters included amplitudes of temperature fluctuations, potential energy per unit mass, and wave drag. These parameters were retrieved at altitudes up to 160 and 100 km from the middle- and near-infrared channels, respectively. Results. A comparison of the data obtained during the global dust storm of Martian year 34 with the corresponding period of Martian year 35 without a storm revealed a reduction of wave activity in mid-latitudes, which is in agreement with previous observations, and enhancement in the polar regions of the southern hemisphere, which was not predicted by simulations with a high-resolution circulation model.

Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy.

Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.

In-situ mapping of iron and titanium with the visible and near-infrared image spectrometer (VNIS) along the Yutu-2 rover traverse on the farside of the moon

In January 2019, China's Chang'E-4 probe successfully landed in the Von Kármán crater of the Moon's farside's South Pole-Aitken basin. Till August 2021, the Yutu-2 rover had worked for >32 lunar days. The Visible and Near-infrared Image Spectrometer (VNIS) is one of scientific payloads on board the rover that can collect in-situ spectral data. This provides a unique perspective for investigating the composition of the lunar surface materials. In this work, a local-scale FeO and TiO2 abundance mapping approach based on the in-situ VNIS data was proposed to utilize the fine spectral-spatial hyperspectral information. Improved FeO and TiO2 abundance inversion models were performed on each unshaded pixel to generate abundance maps. Experimental results obtained on 115 VNIS observation data along the Yutu-2's routing path confirm the effectiveness of the proposed approach, especially in the complex observation scenarios. Comparing with the in-orbit data products and the in-situ abundance results obtained by the reference methods, the effectiveness of the proposed in-situ FeO and TiO2 abundance mapping approach is validated. Furthermore, the proposed approach allows a detailed analysis of chemical properties and spatial distribution of lunar surface materials, and helps to discover abnormal targets according to the serial spectral observations along the rover's routing path, which reveals the great potential in more precise lunar surface material identification and analysis applications.

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement.

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein-nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders.

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing.

Assessement of the Acidity of Progenies From Crosses Between Double Self-fertilized DA 115 D AF AF × LM 2 T AF AF Oil Palm (Elaeis guineensis Jacq) Progenitors in Côte d’Ivoire

Crude palm oil, extracted from the mesocarp of the fruit of the oil palm (Elaeis guineensis Jacq.), is the world’s most widely produced and consumed vegetable oil. However, the food quality of this oil is influenced by its acidification and the negative effects associated with this acidity. Recent work on progenies from the DA 115 D of the Deli population and LM 2 T of the La Mé population involved in seed production has shown variability in oil acidity within and between these progenies, suggesting the possibility of selection to improve palm oil quality in these progenies. To improve the quality of the oil produced in Côte d’Ivoire, progeny from crosses between double self-fertilized progenitors of DA 115 D and LM 2 T origins were evaluated. The acidity of these progenies was determined using the near infrared spectrometer (NIR). The results showed that 4 progenies were homogeneous with low acidities (acidity < 5%) and 9 progenies were heterogeneous with low and medium acidities (5% < acidity < 10%). In addition, only offspring from combinations involving LM 2509 D AF were homogeneous. As a result, the average acidity of each progeny was all below 5%.

A near-infrared laser dispersion spectrometer with phase modulation for open-path methane sensing.

A laser-based open-path dispersion spectrometer for measuring atmospheric methane has been developed with the goal of achieving a very simple architecture, yet enabling molecular dispersion measurements immune to optical power variation. Well-mature, near-infrared photonics components were retained to demonstrate a compact, cost-effective, and low-power consumption dispersion spectrometer. In particular, measurements immune to received optical power variations are demonstrated despite the use of only phase modulation and are supported by the development of the corresponding physical model. The instrument has been validated under laboratory conditions, finding a precision of 2.6 ppb 100m for a 2s measurement, and demonstrated through atmospheric measurements performed continuously over six days with an 86m path length.