Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80-88% TSI, and more than 88% TSI. The 80-88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O₂).gTS^-1 and 92 mL(CH₄).gTS^-1_. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.

Preprocessing NIR Spectra for Aquaphotomics

Even though NIR spectroscopy is based on the Beer–Lambert law, which clearly relates the concentration of the absorbing elements with the absorbance, the measured spectra are subject to spurious signals, such as additive and multiplicative effects. The use of NIR spectra, therefore, requires a preprocessing step. This article reviews the main preprocessing methods in the light of aquaphotomics. Simple methods for visualizing the spectra are proposed in order to guide the user in the choice of the best preprocessing. The most common chemometrics preprocessing are presented and illustrated by three real datasets. Some preprocessing aims to produce a spectrum as close as possible to the absorbance that would have been measured under ideal conditions and is very useful for the establishment of an aquagram. Others, dedicated to the improvement of the resolution of the spectra, are very useful for the identification of the peaks. Finally, special attention is given to the problem of reducing multiplicative effects and to the potential pitfalls of some very popular methods in chemometrics. Alternatives proposed in recent papers are presented.

A high-throughput and cost-effective microplate reader method for measuring persulfates (peroxydisulfate and peroxymonosulfate)

Frequent use of persulfates as oxidants, for in situ chemical oxidation and advanced oxidation processes, warrants the need for developing a fast and efficient method for measuring persulfate concentrations in aqueous samples in the lab and on site. Here, we propose a modified method, based on Liang et al.'s (2008) spectrophotometric method, for measuring both peroxydisulfate (PDS) and peroxymonosulfate (PMS) in the aqueous samples. Our method involves a deep 96-well plate, multi-channel pipettes, a small orbital shaker, and a microplate reader; allowing the preparation and analysis of up to 96 samples in one run. Our proposed method shortens the time by 10 folds, consumes only ∼2% of the original reagents, and generates only ∼2% of the liquid waste compared to the Liang et al.'s method, thus, making our method high-throughput, time-efficient, and cost-effective with reduced environmental impact. The presented microplate reader method is validated in terms of linearity, LOD, LOQ, accuracy, precision, robustness, and selectivity. All the parameters satisfied the acceptance criteria, according to ICH guidelines. The linearity of calibration curves was evaluated by performing the F-test. In general, our method has linear ranges from 20 to 42,000 and 5 to 40,960 μM for PDS and PMS, respectively. Accuracy (% recovery) results suggested that the LOD and LOQ based on the standard deviation of y-intercepts of the regression lines were the most reliable. The LOD/LOQ values for PDS and PMS were 14.7/44.1 and 4.6/14.4 μM, respectively. The proposed method was also modified to work with a standard cuvette spectrophotometer and was validated. A comparison with the UHPLC analysis of PDS showed that our microplate reader method performed equivalently or even outperformed the UHPLC method, in the presence of common groundwater constituents and organic contaminants.

Lung tissue phantom mimicking pulmonary optical properties, relative humidity, and temperature: a tool to analyze the changes in oxygen gas absorption for different inflated volumes

SIGNIFICANCE

Gas in scattering media absorption spectroscopy (GASMAS) enables noninvasive gas sensing in the body. It is developing as a tool for diagnosis and monitoring of respiratory conditions in neonates. Phantom models with relevant features to the clinical translation of GASMAS technology are necessary to understand technical challenges and potential applications of this technique. State-of-the-art phantoms designed for this purpose have focused on the optical properties and anthropomorphic geometry of the thorax, contributing to the source-detector placement, design, and optimization. Lung phantom mimicking the alveolar anatomy has not been included in the existent models due to the inherent complexity of the tissue. We present a simplified model that recreates inflated alveoli embedded in lung phantom.

AIM

The goal of this study was to build a lung model with air-filled structures mimicking inflated alveoli surrounded by optical phantom with accurate optical properties (μa  =  0.50  cm  -  1 and μs'=5.4  cm-1) and physiological parameters [37°C and 100% relative humidity (RH)], and to control the air volume within the phantom to demonstrate the feasibility of GASMAS in sensing changes in pulmonary air volume.

APPROACH

The lung model was built using a capillary structure with analogous size to alveolar units. Part of the capillaries were filled with liquid lung optical phantom to recreate scattering and absorption, whereas empty capillaries mimicked air filled alveoli. The capillary array was placed inside a custom-made chamber that maintained pulmonary temperature and RH. The geometry of the chamber permitted the placement of the laser head and detector of a GASMAS bench top system (MicroLab Dual O2  /  H2O), to test the changes in volume of the lung model in transmittance geometry.

RESULTS

The lung tissue model with air volume range from 6.89  ×  10  -  7  m3 to 1.80  ×  10  -  3  m3 was built. Two measurement sets, with 10 different capillary configurations each, were arranged to increase or decrease progressively (in steps of 3.93  ×  10  -  8  m3) the air volume in the lung model. The respective GASMAS data acquisition was performed for both data sets. The maximum absorption signal was obtained for configurations with the highest number of air-filled capillaries and decreased progressively when the air spaces were replaced by capillaries filled with liquid optical phantom. Further studies are necessary to define the minimum and maximum volume of air that can be measured with GASMAS-based devices for different source-detector geometries.

CONCLUSIONS

The optical properties and the structure of tissue from the respiratory zone have been modeled using a simplified capillary array immersed in a controlled environment chamber at pulmonary temperature and RH. The feasibility of measuring volume changes with GASMAS technique has been proven, stating a new possible application of GASMAS technology in respiratory treatment and diagnostics.