Near-infrared Spectroscopy Technique Research Articles

Rapid and non-destructive prediction of moisture content on intact gayo coffee beans using near infrared spectroscopy

Abstract Presented work explores the potential of Near-Infrared Spectroscopy (NIRS) as a rapid, non-destructive analytical method for predicting moisture content in intact Gayo coffee beans. The moisture content is an essential parameter affecting the quality, processing, and shelf life of coffee beans. While conventional moisture determination techniques involve invasive procedures and considerable time, NIRS emanates as an innovative technique offering non-invasive, swift analysis. Using a robust representative selection of Gayo coffee bean samples, we acquired numerous spectra and employed multivariate analysis to construct a calibration model correlating the spectral data with reference moisture values. The initial results exhibit a high correlation coefficient (r) of 0.93 and ratio prediction to deviation (RPD) index of 3, suggesting that the NIRS technique can effectively predict the moisture content within the beans. This research underpins the potential usage of NIRS which, if validated through further testing, could transform quality control operations in the coffee industry by offering a real-time, non-destructive approach for moisture content determination.

The neural mechanism of communication between graduate students and advisers in different adviser-advisee relationships

Communication is crucial in constructing the relationship between students and advisers, ultimately bridging interpersonal interactions. Only a few studies however explore the communication between postgraduate students and advisers. To fill the gaps in the empirical researches, this study uses functional near-infrared spectroscopy (FNIRS) techniques to explore the neurophysiology differences in brain activation of postgraduates with different adviser-advise relationships during simulated communication with their advisers. Results showed significant differences in the activation of the prefrontal cortex between high-quality and the low-quality students during simulating and when communicating with advisers, specifically in the Broca's areas, the frontal pole, and the orbitofrontal and dorsolateral prefrontal cortices. This further elucidated the complex cognitive process of communication between graduate students and advisers.

Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques.

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.

Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning.

Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination. We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection. Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively. These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies.

A feasibility study investigating cortical hemodynamic changes during infinity walk with fNIRS

This study seeks to explore the correlation between cortical activation and the Infinity Walk pattern, examining how the influence of foot overpronation and footwear may impact motor control. Functional near-infrared spectroscopy (fNIRS), a portable and user-friendly neuroimaging technique, was used to measure hemodynamical changes in six individuals with non-critical pronation degrees. Participants perform the Infinity Walk under various footwear conditions while wearing an fNIRS portable imaging device. Results indicate a consistent hemodynamic pattern in both hemispheres during the Infinity Walk, with no significant differences observed across subjects and footwear conditions in the prefrontal cortex (PFC), pre-motor area, the supplementary motor cortex (PMA & SMC), the primary motor cortex (PMC), and Wernicke’s area (WA). The impact of pronation and footwear on motor control remains inconclusive due to inconsistent hemodynamic patterns. Notably, the activation patterns in Broca’s area (BA) and the temporal gyrus (TG) differ significantly from other brain regions. The balanced hemodynamic responses in the bilateral hemispheres may be attributed to the Infinity Walk’s inherent walking pattern. These findings indicate a need for further investigation into the Infinity Walk to examine the similarities and distinctions in activation patterns within specific brain regions. Additionally, the impact of pronation necessitates more substantial experimental research to establish a correlation between pronation and cortical hemodynamics.

Blueshift and Photoluminescence Enhancement in Broadband Near‐Infrared Emitting Phosphor NaSr2(Al/Ga)Ge5O14: Cr3+ Dependence on the Sc/In Substitution

AbstractCr3+‐activated near‐infrared (NIR) phosphors have gained extensive attention for use in NIR phosphor‐converted light‐emitting diodes (pc‐LEDs), which are required for smart NIR spectroscopy techniques. Nevertheless, obtaining broadband NIR‐emitting phosphors with outstanding thermal stabilities and photoluminescence (PL) efficiencies remains a significant challenge. Herein, considering NaSr2XGe5O14 (X = Al, Ga, Sc, and In) as hosts, which possess a large band gap and multiple sites for the substitution of Cr3+ ions. NaSr2XGe5O14:Cr3+ phosphors are successfully prepared using a solid‐state reaction technology. Upon blue light excitation, the phosphors cover a broad NIR emission band in 650–1100 nm region. Notably, an abnormal blueshift and PL enhancement are observed following the substitution of Al/Ga with Sc/In. In particular, the PL internal quantum efficiency is improved from 34.5% (NaSr2GaGe5O14: Cr3+) to 87.5% (NaSr2ScGe5O14: Cr3+). Moreover, the manufactured NIR pc‐LED exhibited an exceptional photoelectric efficiency of 17.7% with an NIR output power of 10.2 mW at 20 mA, and its potential application in nondestructive detection is demonstrated.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

Spectroscopy and Chemometrics for Conformity Analysis of e-Liquids: Illegal Additive Detection and Nicotine Characterization

Vaping electronic cigarettes (e-cigarettes) has become a popular alternative to smoking tobacco. When an e-cigarette is activated, a liquid is vaporized by heating, producing an aerosol that users inhale. While e-cigarettes are marketed as less harmful than traditional cigarettes, there are ongoing concerns about their long-term health effects, including potential lung damage. Therefore, it is essential to closely monitor and study the composition of e-liquids. E-liquids typically consist of propylene glycol, glycerin, flavorings and nicotine, though there have been reports of non-compliant nicotine concentrations and the presence of illegal additives. This study explored spectroscopic techniques to examine the conformity of nicotine labeling and detect the presence of the not-allowed additives: the caffeine, taurine, vitamin E and cannabidiol (CBD) in e-liquids. A total of 236 e-liquid samples were carefully selected for analysis. Chemometric analysis was applied to the collected data, which included mid-infrared (MIR) and near-infrared (NIR) spectra. Supervised modeling approaches such as partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were employed to classify the samples, based on the presence of nicotine and the targeted additives. This study demonstrates the efficacy of MIR and NIR spectroscopic techniques in conjunction with chemometric methods (SIMCA and PLS-DA) for detecting specific molecules in e-liquids. MIR with autoscaling data preprocessing and PLS-DA achieved 100% classification rates for CBD and vitamin E, while NIR with the same approach achieved 100% for CBD and taurine. Overall, MIR combined with PLS-DA yielded the best classification across all targeted molecules, suggesting its preference as a singular technique.

Achieving the performance luminescence of LaMg0.5Sn0.5O3:Cr3+ by [In3+-Sc3+] co-substitution [Mg2+-Sn4+] and its application to the biodetection and night vision lighting

The exploration of performance luminescence near-infrared (NIR) material is crucial for rapidly developing NIR spectroscopic techniques. Herein, the NIR LaMg0.5Sn0.5O3:Cr3+ phosphor was synthesized by a high temperature solid-state method. And its luminescence performance were improved by [In3+-Sc3+] substituting for [Mg2+-Sn4+]. The half-peak width can be expanded to 211 nm, the emission intensity can reach the seven times of that un-substitution, and the emission intensity at 423 K was maintained at 60 % of that at room temperature, moreover, the internal quantum efficiency can reach 54.2 %. On this basis, the phosphor converted light emitting diodes (pc-LEDs) were achieved by combining La(Mg1-xSn1-xInxScx)0.5O3:Cr3+ with blue LED, importantly, it can be applied to the biodetection and night vision lighting, which indicate its application value.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data

Alfalfa is a forage of vast importance around the world. In the past, near-infrared spectroscopy (NIRS) technique have been explored in the lab to determine quality traits such as fibre content in dried and ground material. During the last decade, portable hyperspectral devices have emerged as a tools for in-field prediction, of not only crop yield but also a large range of quality and physiological markers. The objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions. Reflected spectra were measured in single leaves as well as at the canopy level, then reflectance was related to target parameters such as biomass, leaf pigments, sugars, protein, and mineral contents. Due to their large effect on crop quality parameters, meteorological conditions and phenological stages were included as predictors in the models. We found that meteorological and phenological variables improved the accuracies and percentage of variance explained (R2) for most of the parameters evaluated. Based on R2 values, the best prediction models were obtained for biomass (0.71), sucrose (0.65), flavonoids (Flav) (0.56) and nitrogen (0.70) with normalized root mean squared errors of 0.196, 0.32, 0.087 and 0.08, respectively. These parameters were associated mainly with visible (VIS) (approx. 350–700 nm) and near infrared (NIR) (700–1250 nm) regions of the spectrum. Regarding mineral composition, the best prediction models were developed for P (0.51), B (0.50) and Zn (0.44), associated with the short-wave infra-red (SWIR) region (1250–2500 nm). The results of this study demonstrated the potential of hyperspectral techniques to be used as a base for performing initial evaluations in the field of quality traits in alfalfa crops.

BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification

Effective development and utilization of wood resources is critical. Wood modification research has become an integral dimension of wood science research, however, the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques. So, the development of efficient and accurate wood classification techniques is inevitable. This paper presents a one-dimensional, convolutional neural network (i.e., BACNN) that combines near-infrared spectroscopy and deep learning techniques to classify poplar, tung, and balsa woods, and PVA, nano-silica-sol and PVA-nano silica sol modified woods of poplar. The results show that BACNN achieves an accuracy of 99.3% on the test set, higher than the 52.9% of the BP neural network and 98.7% of Support Vector Machine compared with traditional machine learning methods and deep learning based methods; it is also higher than the 97.6% of LeNet, 98.7% of AlexNet and 99.1% of VGGNet-11. Therefore, the classification method proposed offers potential applications in wood classification, especially with homogeneous modified wood, and it also provides a basis for subsequent wood properties studies.

A narrative review on the use of near-infrared spectroscopy to monitor bladder volume and in vitro validation approaches.

Current guidelines for patients with neurogenic bladder (NB) dysfunction suggest the use of self-intermittent catheterization with adherence to catheterization timers. Due to biorhythmic variability, unpredictable voiding times may occur. As a result, many patients abstain from extended social or work activities, turn to more secluded lifestyles, and generally experience a decrease in quality of life. Being able to know when to void is essential for patients with NB dysfunction. To solve the problem of variable void timings, wearable devices using near-infrared spectroscopy (NIRS) have been emergent and showed potential for bladder volume monitoring. Therefore, in this review, we provided a comprehensive overview of research which implemented NIRS for bladder volume monitoring, and discussed how the researchers validated their device by in-vitro methods and suggested a potential validation approach. A literature search using PubMed and Google Scholar was conducted on February 2023. Publications involving bladder volume monitoring incorporating NIRS technique with in-vitro validation was considered for review. Due to the novelty of NIRS being applied to bladder monitoring, there are a few possibilities to effectively validate this technique through in-vitro methods. Ballistics gel, which has been proven to be a versatile material for applications involving ultrasound, could be a suitable material when constructing a bladder phantom for in-vitro validation of NIRS technology. By outlining a more standardized in-vitro validation model based on ballistics gel, we hope to facilitate development in this field towards a more accurate and robust NIRS-based bladder monitoring device.

An overview of the use of near-infrared spectroscopy (NIRS) in assessing silage composition: review

In this study, we confidently evaluated the effectiveness of using infrared spectroscopy for assessing silage, and observed the results obtained with portable equipment. Several factors alter this ensiled material's composition and quality, and monitoring is necessary to create an accurate diet formulation. Analysis using near-infrared spectroscopy (NIRS) this technique is the wavelength of the near-infrared region and measuring the difference in the amount of energy emitted by the equipment and the energy reflected by the sample to the detector, be measured by different bands create a spectrum of this sample. In the nutrition of ruminants, mainly confined animals, silage is the main source of fiber used in their diet and the NIRS technique has been widely used to evaluate its chemical composition the digestibility, within other parameters. Use with benchtop equipment in the laboratory or with portable equipment offers real-time results on the farm, allows diet variability to be monitored, and assists in decision maki. However, further research into the use of this equipment to assess its accuracy and which type of equipment is best to use is recommended.

Near-infrared spectroscopy and machine learning algorithms for rapid and non-invasive detection of Trichuris.

Trichuris trichiura (whipworm) is one of the most prevalent soil transmitted helminths (STH) affecting 604-795 million people worldwide. Diagnostic tools that are affordable and rapid are required for detecting STH. Here, we assessed the performance of the near-infrared spectroscopy (NIRS) technique coupled with machine learning algorithms to detect Trichuris muris in faecal, blood, serum samples and non-invasively through the skin of mice. We orally infected 10 mice with 30 T. muris eggs (low dose group), 10 mice with 200 eggs (high dose group) and 10 mice were used as the control group. Using the NIRS technique, we scanned faecal, serum, whole blood samples and mice non-invasively through their skin over a period of 6 weeks post infection. Using artificial neural networks (ANN) and spectra of faecal, serum, blood and non-invasive scans from one experiment, we developed 4 algorithms to differentiate infected from uninfected mice. These models were validated on mice from a second independent experiment. NIRS and ANN differentiated mice into the three groups as early as 2 weeks post infection regardless of the sample used. These results correlated with those from concomitant serological and parasitological investigations. To our knowledge, this is the first study to demonstrate the potential of NIRS as a diagnostic tool for human STH infections. The technique could be further developed for large scale surveillance of soil transmitted helminths in human populations.

Abstract 14998: Assessment of Microcirculatory Dysfunction by Measuring Subcutaneous Tissue Oxygen Saturation Using Near-Infrared Spectroscopy in Patients With Circulatory Failure

Background: Patients with circulatory failure have been treated with the goal of normalizing systemic hemodynamic parameters such as blood pressure, central venous pressure, urine output, and central venous oxygen saturation. However, microcirculatory dysfunction remains even after improvement of these indices, suggesting a loss of hemodynamic coherence between the macrocirculation and the microcirculation. Recently, the usefulness of near-infrared spectroscopy (NIRS) in assessing the microcirculation has been reported. Aim: We measure regional tissue oxygenation saturation (rSO 2 ) at 3 - 5 mm subcutaneously in patients with circulatory failure using a finger-mounted tissue oximeter with the NIRS technique (Toccare: Astem Co., Ltd., Kawasaki, Japan) to investigate its relationship with microcirculation. Methods: This is a single-center prospective observational cohort study of patients with circulatory failure (systolic blood pressure ≤ 90 mmHg or lactate ≥ 2 mmol/L at presentation) transported to the emergency department of Nihon University Hospital from 15 June 2020 to 31 March 2022. We measured rSO 2 in the forehead, thenar eminence, thumb and knee using Toccare and assessed the relationship between rSO 2 and blood pressure, acute physiology and chronic health evaluation (APACHE) II score, sequential organ failure assessment (SOFA) score and mottling score. Result: In patients with circulatory failure (n = 38), rSO 2 at each site showed no correlation with blood pressure, a negative correlation with SOFA score and APACHE II score and a positive correlation with mottling score. These significant correlations were also best observed in the knee. Conclusion: In patients with circulatory failure, rSO 2 measured with Toccare showed no correlation with blood pressure but a significant negative correlation with the severity of organ damage. The knee is possibly the optimal measurement site for monitoring microcirculatory dysfunction and determining the effectiveness of treatment.

Determination of Multicomponents in Rubi Fructus by Near-Infrared Spectroscopy Technique

Objective. Rubi Fructus (RF) is a dry aggregate fruit of Rubus (Rosaceae). It has shown significant pharmacological effects such as anti-oxidation, hypoglycemic, and anti-inflammatory. A combination of near-infrared (NIR) spectroscopy and partial least squares regression (PLSR) under seven different spectral data preprocessing techniques was used to determine the performance of quantitative analysis correction models which employed moisure, ellagic acid, and total flavonoids as indicators of RF. Methods. Ninety-seven different RF batches were collected for NIR spectra. By using primary analysis techniques such as drying method, high-performance liquid chromatography (HPLC), and ultraviolet visible spectrophotometry (UV-Vis), the contents of moisure, ellagic acid, and total flavonoids were determined. The NIR spectral data and the primary analysis method data were correlated through PLSR. Seven methods were used for pretreating the spectral data, including no spectral pretreatment, first derivative, standard normalized variate, multiple scattering corrections, elimination of constant offset, and minimum maximum normalization. The quantitative analysis correction models adopted PLSR chemometrics for moisture, ellagic acid and total flavonoids were developed, and their effectiveness was evaluated using the correlation coefficient (R), ratio of prediction to deviation (RPD), and root mean square error (RMSE). Results. The first derivative was combined with variable standardization, elimination of constant offset, and multiple scattering corrections, respectively, to pretreat the PLSR models for moisture, ellagic acid, and total flavonoids. The R-values of the PLSR models for moisture, ellagic acid, and total flavonoids were, respectively, 0.9788, 0.9468, and 0.9748, all of which were higher than 0.90, and the RPD values were 4.9, 3.1, and 4.5, respectively, which were all larger than 3.0. The RMSE ratios of the calibration set and the test set were 0.98, 0.94, and 1.0, respectively. Conclusion. The R-values of the NIR-PLSR models for moisture, ellagic acid, and total flavonoids are all greater than 0.90 after suitable pretreatments, indicating that the models are reliable. The RPD values are more than 3.0, which indicate that the models are good and useable for quality control. The RMSE ratios are closed to 1, indicating that the calibration set and test set had same distribution and the models were not overfitting indicating good predictability.

Near-infrared spectroscopy bioprobe estimation of metabolites’ responses to Pb2+ in Cladophora rupestris

Near-infrared (NIR) spectroscopy was used as a bioprobe to estimation chlorophyll, total sugar, proline, and metallothionein (MT) response to Pb2+ (0, 1, 2.5, 5, 7.5, 10, and 20 mg/l) in Cladophora rupestris. Spectra between 3775.94 and 12315.19 cm−1 were analysed using a partial least-squares regression (PLS). Principal component analysis (PCA) was performed by evaluating the distance between the C. rupestris samples treated with different concentrations of Pb2+. Results showed that 4000–7500 cm−1 of raw NIR spectra from C. rupestris was linked to carbohydrates and stretching of N–H, and the O–H overtone was associated with proline and MT. NIR spectroscopy technique coupled with PLS quantitative analysis model has high potential. Multiplicative scatter correction and standard normal variate combining first-order derivative with S-G smoothing filter are superior to the other models. The root mean square error of cross of chlorophyll, total sugar, proline, and MT was 0–1, indicating the accuracy of the model is high. The Rc2 values of chlorophyll, total soluble sugar, proline, and MT between measured and predicted values were higher than 0.99. PCA analysis indicate clear boundaries in the spatial distributions of seven Pb2+ concentrations, indicating that NIR spectroscopy has high potential as a tool assisting bioprobes for estimating metabolites’ responses to Pb2+ in Cladophora rupestris.

Performance evaluation of NIR spectrophotometer simulating in-line acquisition for moisture content prediction of woodchips and comparison with hand-held NIR spectrophotometer

The European Green Deal, together with the current political and economic background, aims at improving the use of renewable energy resources rather than fossil fuel dependence. Woodchip could be considered as a valid and widely available alternative, but the high heterogeneity of its properties must be evaluated according to the important influence in each step of the supply chain to combustion process. Moisture content is the main parameter involved in the quality characterization because it influences the energetic (it reduces the biomass calorific value in combustion), the management (during harvesting, handling, and storage steps), and the economic (price based on the load, affected by bulk density) aspects of woodchip utilization. Continuous monitoring of moisture content results essential, but laboratory analyses are time-consuming, destructive, and need economic efforts in terms of instruments and skilled personnel. Near infrared Spectroscopy (NIRS) is a fast, friendly-use, and alternative method to qualitative characterization, based on non-destructive interaction between wavelength and chemical components. So, the aim of this project is to evaluate the prediction performance of moisture content using a portable NIRS adapted to in-line utilization. Then, the results have been compared with the already implemented prediction models of a different portable NIRS, applying statistics to assess the industrial woodchip quality, but also the Tukey’s Test method to evaluate the replicates effect. Considering the general consistency of industrial woodchip quality and the use of three different replicates approach, results show good performance ranging from 0.64 to 0.86 as determination coefficient, with errors of prediction from 4.44 to 2.88%, confirmed by a prediction bias below the 2.5% for more than 50% of the samples. Tukey’s Test shows that replicates adequately described samples variability thanks to constant and uniform analysis that dilutes the error. Furthermore, accepting a minimum error threshold, no investigation on replicates feasibility is needed, reducing time and economic efforts. In conclusion, the crucial demand of moisture control meets the several advantages of NIRS technique, with the potential in-line application to obtain real-time quality tracing and high-performance energy production.

Rapid detection of multi-indicator components of classical famous formula Zhuru Decoction concentration process based on fusion CNN-LSTM hybrid model with the near-infrared spectrum

Traditional Chinese Medicine (TCM) is a collective term for the medicines of all ethnic groups in China, including Han Chinese and ethnic minority medicines, reflecting the Chinese people's understanding of life, health and disease, and is a system of medicine with a long historical tradition and unique theories and technical methods. Due to the nonlinearity, large hysteresis, and strong interference in the concentration process used in the production of traditional Chinese medicine, it is challenging to use just one conventional control method to achieve the desired control effect. The majority of traditional quality control methods used in processing the samples will result in the loss of the active ingredient and in the detection of impurities. Monitoring of multiple critical quality attributes (CQAs) is crucial for the overall process of the index ingredient. In this study, based on the above background, fast and nondestructive near-infrared spectroscopy (NIRS) technique incorporating convolutional neural network (CNN) and long-short-term memory network (LSTM) was utilized to construct a quantitative model based on the seven characteristic components, puerarin, 3′-hydroxy puerarin, 3′-methoxy puerarin, puerarin apigenin, daidzin, glycyrrhizin and glycyrrhizic acid, during the concentration process. The results showed that the relative mean error (MAPE) values of the different components ranged from 0.7% to 2.8% and R2 from 0.981 to 0.992 based on the proposed CNN-LSTM model. The results showed that the combined model of near-infrared spectroscopy fused with convolutional neural network (CNN) and long-short-term memory network (LSTM) was feasible for accurate and rapid monitoring of the concentration of the classic famous prescription Zhuru Decoction (ZRD) during the decoction process and that the method could provide a reference value for rapid nondestructive testing of traditional Chinese medicine compound formulas.