Mid-infrared Spectroscopy Research Articles

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.

High-power long-picosecond pulse fiber laser at 2 μm with a narrow spectral width

Herein, stable mode-locked pulse trains with an ultra-narrow linewidth at the 2 μm region were obtained in a compact all polarization-maintaining fiber oscillator, which can directly deliver nearly transform-limited pulse with a duration of ∼105.6 ps and a spectral width of ∼86 pm. Relying on the multistage Tm-doped fiber amplifier chains, the output power of ∼19.42 W with a narrow linewidth and the maximum output power of ∼100.7 W were obtained, respectively. The narrow linewidth pulse laser may have the promising potential applications in mid-infrared spectroscopy and polymer processing.

Identification of geographical origin and adulteration of Northeast China soybeans by mid-infrared spectroscopy and spectra augmentation

Identification of geographical origin and adulteration of Northeast China soybeans by mid-infrared spectroscopy and spectra augmentation

Sub-wavelength gratings in silicon photonic devices for mid-infrared spectroscopy and sensing

Mid-infrared spectroscopy enabled by silicon photonics has received great interest in recent years as a pathway for a scalable sensing technology. The development of such devices would realise inexpensive and accessible instrumentation for a wide variety of uses over numerous fields. However, not every sensing application is the same; to produce sensors for real-world scenarios, engineers need flexibility in device design but also need to maintain compatibility with scalable fabrication processes. Sub-wavelength gratings can offer a solution to this problem, as they enable the engineering of optical properties using standard fabrication techniques and without requiring new materials. By using sub-wavelength gratings, specific design approaches can be tailored to different applications, such as increasing the interaction of a sensor with an analyte or broadening the bandwidth of an integrated photonic device. Here, we review the development of sub-wavelength grating-based devices for mid-infrared silicon photonics and discuss how they can be exploited for spectroscopic and sensing devices.

Evaluation of an electrochemical sensor and comparison with spectroscopic approaches as used today in practice for harm reduction in a festival setting-A case study: Analysis of 3,4-methylenedioxymethamphetamine samples.

More and more countries and organisations emphasise the value of harm reduction measures in the context of illicit drug use and abuse. One of these measures is drug checking, a preventive action that can represent a quick win by tailored consultation on the risks of substance use upon analytical screening of a submitted sample. Unlike drop-in centres that operate within a fixed setting, enabling drug checking in a harm reduction context at events requires portable, easy to use analytical approaches, operated by personnel with limited knowledge of analytical chemistry. In this case study, four different approaches were compared for the characterisation of 3,4-methylenedioxymethamphetamine samples and this in the way the approaches would be applied today in an event context. The four approaches are mid-infrared (MIR), near-infrared, and Raman spectroscopy, which are today used in drug checking context in Belgium, as well as an electrochemical sensor approach initially developed in the context of law enforcement at ports. The MIR and the electrochemical approach came out best, with the latter allowing for a direct straightforward analysis of the percentage 3,4-methylenedioxymethamphetamine (as base equivalent) in the samples. However, MIR has the advantage that, in a broader drug checking context, it allows to screen for several molecules and so is able to identify unexpected active components or at least the group to which such components belong. The latter is also an important advantage in the context of the growing emergence of new psychotropic substances.

Automized inline monitoring in perfused mammalian cell culture by MIR spectroscopy without calibration model building.

Process Analytical Technologies (PATs) are taking a key role in the run for automatization in the biopharmaceutical industry. Spectroscopic methods such as Raman spectroscopy or mid-infrared (MIR) spectroscopy are getting more recognition in the recent years for inline monitoring of bioprocesses due to their ability to measure various molecules simultaneously. However, their dependency on laborious model calibration making them a challenge to implement. In this study, a novel one-point calibration that requires a single reference point prior to the inline monitoring of glucose and lactate in bioprocesses with MIR spectroscopy is assessed with 22 mammalian cell perfusion (PER) processes in two different scales and four different products. Concentrations are predicted over all PERs runs with a root mean square error (RMSE) of 0.29g/L for glucose and 0.24g/L for lactate, respectively. For comparison conventional partial least square regression (PLSR) models were used and trained with spectroscopic data from six bioreactor runs in two different scales and three products. The general accuracy of those models (RMSE of 0.41g/L for glucose and 0.16g/L for lactate) are in the range of the accuracy of the one-point calibration. This shows the potential of the one-point calibration as an approach making spectroscopy more accessible for bioprocess development.

Chemometric models for high-throughput biomass grading of the kelp Ecklonia radiata, using mid-infrared (MIR) and near-infrared (NIR) spectroscopy

The kelp Ecklonia radiata is a target species for aquaculture in the southern hemisphere, but the biomass quality varies both spatially and temporally, making high-throughput chemometric methods essential for biomass grading. We built partial least squares regression models and partial least squares discriminant analysis models using mid-infrared and near-infrared spectroscopy, based on 162 calibration samples and 24 validation samples. The near-infrared models generally outperformed the mid-infrared models based on both predictions and sensitivity to outliers. Near-infrared partial least squares regression models were highly accurate for predicting glucose, alginate, phlorotannins, and carbon content (relative prediction error < 8 %) and the near-infrared partial least squares discriminant analysis model could accurately predict region of origin for Wellington samples (zero false negatives and one false positive). Overall, our results demonstrate that near-infrared and mid-infrared spectroscopy are valuable tools for biomass grading of raw seaweed samples and can be used for establishing provenance.

Mid-infrared dual-comb spectroscopy for rapid temperature distribution characterization.

Due to the influence of chemical reactions, phase change, and other phenomena, the combustion system is a complicated high-temperature environment. Therefore, the spatio-temporally resolved monitoring of the temperature field is crucial for gaining a comprehensive understanding of the intricate combustion environment. In this study, we proposed a fast and high-precision temperature measurement technique based on mid-infrared (MIR) dual-comb spectroscopy with a high spectral resolution and fast refresh rate. Based on this technique, the spatio-temporally resolved measurement of a non-uniform temperature field was achieved along the laser path. To verify the capability of DCS for temperature measurement, the bandhead ro-vibrational lines of the CO2 molecule were acquired, and the 1-σ uncertainty of the retrieved temperature was 3.2°C at 800°C within 100 ms. The results demonstrate the potential of our fast and high-precision laser diagnostic technique which can be further applied to combustion kinetics.

Use of milk mid-infrared spectra to predict serum non-esterified fatty acid concentrations in Chinese Holstein cows

Negative energy balance (NEB) in high-yielding cows during the peripartum period raises the risk of postpartum diseases. High-level concentration of non-esterified fatty acid (NEFA) is a good indicator of excessive NEB. The current low-cost and high-throughput mid-infrared (MIR) spectroscopy method is gradually applied to predict NEFA concentrations for NEB identification. The objective of this study was to compare different pre-processing methods and analysis models for optimal predictions of serum NEFA using milk MIR spectra. Four spectral pre-processing methods: standard normal variate, first-order derivative (FD), second-order derivative, and Savitzky-Golsy convolution smoothing, and four prediction models: partial least squares regression, ridge regression, lasso regression (LassoR), and random forest regression were investigated. In total, 366 collected serum and milk samples within the 1–7 weeks postpartum were randomly divided into the training (70%) and test (30%) sets for cross-validations. The results showed that the combined strategy of FD-LassoR model when parity and days in lactation information were considered resulted in the highest R2 = 0.643, RMSE = 0.153 mmol/L, and highest residual predictive deviation = 1.665 of predictions on the test set. In addition, R2 and RMSE values of FD-LassoR combined with other information were still higher than the other four prediction scenarios. Therefore, our study enables the optimal prediction of serum NEFA concentrations using milk MIR spectra in the further research and practical applications.

Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.

Discriminating milk storage time with mid-infrared spectra combined with machine learning

Classification models for rapid discrimination of milk storage time were developed. Buffalo raw milk (BuR) and bovine raw (BoR), pasteurised (BoP) and ultra-high temperature sterilised (BUHT) milk were analysed by mid-infrared (MIR) spectroscopy at different storage times. A total of 175 (44), 96 (24), 252 (64) and 383 (97) samples were used in the developed (validated) models of BuR, BoR, BoP and BUHT milk, respectively. Seven, two, five, and three spectral regions were selected for BuR, BoR, BoP and BUHT, respectively, to develop their models, which reflect changes in milk protein, fat, lactose and urea nitrogen. Accuracies of the optimised models for BuR, BoR, BoP and BUHT milk were 0.95, 1.00, 0.89, and 0.93, respectively, in the validation dataset. All optimal models were obtained by machine learning methods. This preliminarily study shows that MIR combined with machine learning can rapidly identify the storage time of these four types of milk.

The research on the molecular spectroscopic recognition mechanism of microplastics in typical agricultural media

To study the identification mechanism of microplastics in agricultural environmental media by molecular spectroscopy, two typical media, soil and fishmeal, were selected for this study. Three common microplastics, PE, PP, and PS, were used as research objects. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric methods were used to explore microplastics' identification and analysis effects in different agricultural media and reveal their identification mechanisms. PCA analysis revealed that different soil types would affect the identification results of microplastics. PLS-DA discriminant analysis showed that the accuracy of NIR spectroscopy technology in identifying microplastics in different types of soil decreased in the order of sand > loam > clay. In contrast, the rule of MIR spectroscopy technology was the opposite. The sensitivity and specificity of the three microplastics in the NIR model of sand and the infrared spectroscopy model of fishmeal reached 1.000. NIR spectroscopy technology is suitable for identifying microplastics in soil, while infrared spectroscopy technology is more suitable for identifying microplastics in fish meal. Furthermore, based on the VIP values of each wavelength point in the spectrum, the characteristic bands that have essential contributions to identifying microplastics in soil were screened out. The NIR spectra of 4500–4300 cm−1, 4300–3900 cm−1 and 7100–5800 cm−1 are the most essential characteristic bands for identifying microplastics in clay, loam, and sand, respectively. The MIR of 3000–2900 cm−1 and 700–650 cm−1 were the most essential characteristic bands for identifying microplastics in soils, and the overlap of the characteristic spectra of the three soils reached 59.45%. The NIR spectra of 6050–5600 cm−1, 4700–4000 cm−1 and the MIR spectra of 2300–1900 cm−1 and 800–400 cm−1 are the most essential characteristic bands for the identifying microplastics in fishmeal. This study provides a more suitable technical "solution" for identifying microplastics in environmental media, which is of great significance for improving the accuracy of molecular spectroscopic identification in environmental media and clarifying the interaction between microplastics and environmental media.

Application of mid-infrared spectroscopy for the quantitative and qualitative analysis of organic matter in Holocene sediment records

The organic matter composition of lake sediments influences important in-lake biogeochemical processes and stores information on environmental changes. Extracting this information is notoriously difficult because of the complexity of the organic matter matrix, which routinely imposes trade-offs between high temporal and analytical detail in the selection of methods of analysis. Here, we demonstrate the potential of diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) for achieving both of these objectives using untreated bulk samples from two Holocene lake-sediment cores from central Sweden. We develop quantitative models for sediment total organic carbon (TOC) with the same predictive abilities as models based on samples diluted with KBr and qualitatively characterize the organic matter using a spectra processing-pipeline combined with principal component analysis. In the qualitative analysis we identified four organic matter sub-fractions and the interpretation of these is supported and further advanced with molecular data from pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Within these organic fractions, compound groups such as aromatics, lignin, aliphatics, proteins and polysaccharides were identified by means of DRIFTS and the analyses and processes outlined here enables rapid and detailed quantitative and qualitative analysis of sediment organic matter. The DRIFTS approach can be used as stand-alone method for OM characterization with high temporal resolution in Holocene sediment records. It may also function as a screening process for more specific analyses of sample subsets, such as when coupled with pyrolysis-GC/MS to further tease apart the OM composition, identify sources and determine degradation status.

Molecular structure of dissolved organic carbon in a sandy soil receiving contrasting quality organic residues

Decomposing plant residues release plant-derived dissolved organic carbon (DOC) which can be transformed by microorganisms resulting in microbial-derived DOC (DOC biological function). The adsorbability of DOC to mineral surfaces, determined by DOC chemistry (DOC chemical function), can affect its vertical distribution down soil profiles (D). This study investigated the temporal characteristics (T) and functions (bioavailability, adsorbability) of DOC across the profile of a sandy soil by using chemically contrasting quality (Q) residues: 1) high-cellulose rice straw (RS), 2) nitrogen-rich groundnut stover (GN), 3) lignin-rich dipterocarp leaf litter (DP), and 4) medium-nitrogen and -lignin tamarind leaf litter (TM). Soil samples were collected at two depths (0–15 cm and 60–80 cm) two and eight weeks after residue application, and water-extractable DOC was determined. For the first time, the chemical structure of the extracted DOC was characterized using pyrolysis coupled with mid-infrared spectroscopy (Py-MIRS). The dominant functional groups found in all DOC samples were aromatic, carboxyl, and polysaccharide compounds. The interactive effects of Q, T, and D (soil depth) influenced DOC-polysaccharides, with the most pronounced effect observed in the RS treatment during the early stage, indicating that DOC-polysaccharides were mainly plant-derived. Evidence of partial adsorption of DOC-aromatics and DOC-carboxyls was found in topsoils. These high molecular weight-DOC functional groups may have displaced adsorbed polysaccharides, resulting in higher subsoil DOC-polysaccharides than topsoil, especially under DP and TM. During the later stage, topsoil DOC-polysaccharides were negatively related to microbial metabolic quotient (qCO2) (R2 = 0.938). This relationship led to our hypothesis that the dominant source of DOC-polysaccharides, particularly in the TM treatment, was of microbial origin. A novel discovery from this study is that the chemical quality of plant residues, which serve as the source of DOC, determines the origin of the polysaccharides in DOC. This origin transitioned from plant-derived to microbial-derived as decomposition progressed. Importantly, the presented findings were the first of its kind based on a field experiment.

Robust plastic waste classification using wavelet transform multi-resolution analysis and convolutional neural networks

Mid-infrared spectroscopy (MIR) using photon up-conversion provides advantages over near-infrared spectroscopy (NIR) for plastic waste recycling, including comparable data collection speed and the ability to detect black plastics. However, high-speed MIR spectra suffer from the presence of significant noise. While convolutional neural networks (CNNs) have been utilized for accurate classification of noisy MIR spectra, the analysis of extracted features by the CNN has received less attention. In this study, we analyzed features extracted by a CNN from high-speed MIR spectra collected at 200 spectra per second. Visualizing salient features through the Grad-CAM method revealed that, although the CNN model achieved 100% accuracy, the predictions were not reliable or robust, as the model is susceptible to noise interference. To address this limitation, we propose a wavelet transform-based multi-resolution analysis (MRA) as a preprocessing method for noisy MIR spectra. We show that MRA reconstruction effectively captures features related to characteristic IR peaks, enabling the CNN model to extract informative features from noisy MIR spectra and significantly improves the prediction fidelity and robustness.

Thermal, structural and spectroscopic properties of powder sheep milk obtained by different drying methods

The thermal, microstructural and spectroscopic properties of sheep's milk powder submitted to atomisation and freeze-drying were evaluated. Composition analysis, mid-infrared spectroscopy (MIR), scanning electron microscopy (SEM) and thermal analysis were performed. The MIR spectra showed similar vibration modes associated with each peak, but with a difference in absorption intensity. The SEM images revealed that the milk powder obtained by the atomisation method produced particles with a spherical and wrinkled shape, while the particles obtained by the freeze-drying method showed a continuous structure. Thermal analysis indicated one endothermic peak and four exothermic peaks for atomised milks, while for freeze-dried milks one endothermic peak and three exothermic peaks. MIR analysis showed that the milk powder obtained through atomisation had higher contents than the milk obtained through freeze-drying. This confirms that the atomisation method is more suitable for obtaining milk powder, mainly because it is a cost-effective technique compared with freeze-drying.

Portable Infrared-Based Glucometer Reinforced with Fuzzy Logic.

Diabetes mellitus (DM) is a chronic metabolic condition characterized by high blood glucose levels owing to decreased insulin production or sensitivity. Current diagnostic approaches for gestational diabetes entail intrusive blood tests, which are painful and impractical for regular monitoring. Additionally, typical blood glucose monitoring systems are restricted in their measurement frequency and need finger pricks for blood samples. This research study focuses on the development of a non-invasive, real-time glucose monitoring method based on the detection of glucose in human tears and finger blood using mid-infrared (IR) spectroscopy. The proposed solution combines a fuzzy logic-based calibration mechanism with an IR sensor and Arduino controller. This calibration technique increases the accuracy of non-invasive glucose testing based on MID absorbance in fingertips and human tears. The data demonstrate that our device has high accuracy and reliability, with an error rate of less than 3%, according to the EGA. Out of 360 measurements, 97.5% fell into zone A, 2.2% into zone B, and 0.3% into zone C of the Clarke Error Grid. This suggests that our device can give clinically precise and acceptable estimates of blood glucose levels without inflicting any harm or discomfort on the user.

Species discrimination of multiple botanical origins of Fritillaria species based on infrared spectroscopy, thin layer chromatography-image analysis and untargeted metabolomics

BackgroundFritillaria Bulbus (FB), a precious medicinal herb renowned for its heat-clearing, lung-moistening, cough-relieving and phlegm-eliminating effects. In pursuit of profits, unscrupulous merchants have engaged in the substitution or adulteration of valuable varieties with cheaper alternatives. It is, therefore, urgent to develop effective technical approaches to identify FBs from adulterants. MethodsThis paper employed infrared spectroscopy (IR), thin layer chromatography-image analysis (TLC-IA), and untargeted metabolomics techniques to discriminate ten species of FBs. ResultsFive species of FBs were successfully differentiated using mid-infrared spectroscopy. Furthermore, the power of TLC-IA technology allowed the differentiation of five species of FBs and two origins of FCBs (Fritillariae Cirrhosae Bulbus). Remarkably, through the application of untargeted metabolomics technique, the precise discrimination of five species of FBs, as well as three origins of FCBs were accomplished. Moreover, a comprehensive identification of 101 markers that reliably distinguished diverse FBs was achieved through the employment of untargeted metabolomics technique. ConclusionThe investigation presented powerful means of detection for assuring the quality control of Fritillaria herbs.

Machine Learning-Assisted Near- and Mid-Infrared spectroscopy for rapid discrimination of wild and farmed Mediterranean mussels (Mytilus galloprovincialis)

The objective of this study was to investigate the ability to discriminate between wild and farmed Mediterranean mussels (Mytilus galloprovincialis) using machine learning-assisted near-infrared (NIR) and mid-infrared (MIR) spectroscopy. Mussels are of significant global importance in aquaculture due to their nutritional characteristics, encompassing a rich source of protein, essential fatty acids, various vitamins, and abundant minerals. Additionally, their ease of farming adds to their value as a desirable aquaculture species. The mussels' capacity to reflect environmental quality attributes makes them valuable as biomonitoring agents. However, differences in nutritional composition may arise between wild mussels harvested from natural marine hard-bottoms and those farmed in open artificial systems in the sea. In this study aimed at distinguishing between the two types of mussels, the classification models were created, and the most accurate results were achieved using the FT-MIR spectral data extracted from the interior part of the mussels, while the performance of FT-MIR data obtained from the mussels' shells was slightly lower, with the accuracy of 92% and R2 of 0.87. Still, the accuracies of all the classification models were over 90%. The Ensemble model, trained using FT-MIR spectra from the interior part of the mussel, achieved an accuracy of 98.4%, surpassing the performance of other variable sets. In both NIR and MIR models, spectra from the mussels' interior provide better discrimination than spectra from the outer shell.

Exploring the heights: Impact of altitude on dairy milk composition

The aim of the present study was to assess the impact of altitude on the quality of milk from dairy cows housed in small-scale farms in alpine areas. Therefore, a dataset comprising 5,680 bulk milk samples from 32 farms located at different geographical altitudes was considered. Milk traits such as fat, protein, lactose, free fatty acids, casein, milk urea nitrogen content, pH value, and somatic cell score were examined in the laboratories of the South Tyrolean Dairy Association using mid-infrared spectroscopy. The data were analyzed using a statistical model that considered altitude category, grazing practice, housing system, and season of milk analysis as fixed effects. The findings revealed a positive association between altitude and milk fat, free fatty acids, and somatic cell count. Conversely, lactose content, milk urea nitrogen, and pH value in milk from cows kept on farms at higher altitude (>1,200 m above sea level) showed a negative relationship with altitude. Farms located at altitudes above 1,200 m exhibited higher fat, protein, urea, and somatic cell content than farms located at lower levels. The results provide new insights into a production effect that has so far received little attention and should be considered in farm management (e.g., feeding management, breed decision) to ensure animal health and the associated animal welfare as well as the productivity of dairy cows reared in traditional small-scale mountain dairy farms.