Near-infrared Spectroscopy Information Research Articles

Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network.

Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.

A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques

Food authenticity and traceability and climate change are key scientific issues that must be addressed in response to the food crisis in 2050. Lanmaoa asiatica mushroom is an expensive and nutritious fungi-based diets resource, it is necessary to identify its geographical origin and explore the impact of the climate on it. Thus, the purpose of this study is to establish a fast and accurate geographical traceability model based on L. asiatica mushrooms chemical information collected by near-infrared spectroscopy (NIRS) technology, and screen out key climate variables by competitive adaptive reweighted sampling (CARS) algorithm. Based on the NIRS information of L. asiatica mushrooms, two-dimensional correlation spectroscopy (2D-COS) images were generated and a residual neural network (ResNet) image recognition model was established to identify the geographical origin of L. asiatica mushrooms. The accuracy of training set and test set of ResNet model is 100%, and the loss value is 0.052, which indicates that the model is effective. In addition, the CARS algorithm was used to select the feature variables from 105 climate variables. Four important variables (February, March, and April precipitation and January minimum temperature) related to NIRS difference of L. asiatica mushroom were obtained by CARS algorithm. The results can provide a fast and accurate method for food authenticity and traceability research, and provide an innovative idea for screening key climate factors.

Development of analytical method associating near-infrared spectroscopy with one-dimensional convolution neural network: a case study

Near-infrared spectroscopy (NIRS) is an outstanding detection technology with fast, nondestructive, on-line characteristics, which has been used in many fields to achieve qualitative/quantitative detection of products. This study proposes an analysis method that combines a one-dimensional convolution neural network (1-CNN) with NIRS to select a characteristic wavelength for a subsequent qualitative/quantitative analysis, while considering fully dimensional NIRS information. Batch normalization (BN) and an exponential decay learning rate are embedded into the training stage of the 1-CNN which improves performance and reduces the risk of overfitting. The proposed method is used for NIRS of dried matsutake samples from four regions in China, and a geographical origin identification model is established for the matsutake. The developed 1-CNN model achieves better classification accuracy than traditional models. The experimental results demonstrate that the proposed 1-CNN achieves good generalization and can act as an alternative method for analyzing NIRS data.

Effect of Blood Flow on Hemoglobin and Myoglobin Oxygenation in Contracting Muscle Using Near-Infrared Spectroscopy.

Insufficient O2 delivery to, and uptake by skeletal muscle can produce mobility limitations for patients with chronic diseases. Near-infrared spectroscopy (NIRS) can be used to noninvasively quantify the balance between skeletal muscle O2 delivery and utilization during contraction. However, it is not clear how the oxygenated or deoxygenated NIRS signal should be used to assess muscle O2 changes. This issue is related to the fact that the contributions of hemoglobin (Hb) and myoglobin (Mb) cannot be distinguished. This conundrum can be resolved by quantitative analysis of experimental data by computer simulations with a mechanistic, mathematical model. Model simulations distinguish dynamic responses of the oxygenated (HbO2, MbO2) and deoxygenated (HHb, HMb) contributions to the NIRS signal components (HbMbO2, HHbMb). Simulations of muscle O2 uptake and NIRS kinetics correspond closely to published experimental data (Hernández et al.,J Appl Physiol 108: 1169-1176, 2010). Simulated muscle O2 uptake and oxygenation kinetics with different blood flows indicate (1) faster O2 delivery is responsible for slower muscle oxygenation kinetics; (2) Hb and Mb contributions to the HbMbO2 are similar (40-60%); and (3) Hb and Mb contributions to the HHbMb are significantly different, 80% and 20%, respectively. The effect of slow blood flow kinetics on oxygenated Hb and Mb contributions is minimal. However, the effect on the imbalance between O2 delivery and utilization rates causes significant overshoots and undershoots of deoxygenated Hb and Mb contributions. Model analysis in combination with NIRS measurements and information on hemodynamic and microvascular distribution can help to determine the use of NIRS signal in evaluating the factors limiting exercise tolerance in health and disease states.

Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials.

Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data.

Comparing laser induced breakdown spectroscopy, near infrared spectroscopy, and their integration for simultaneous multi-elemental determination of micro- and macronutrients in vegetable samples

Laser-induced breakdown spectroscopy (LIBS) is an appealing analytical technique for simultaneous multi-elemental analysis. Near-infrared spectroscopy (NIRS) has also been suggested for the same purpose, mainly for vegetable samples. However, LIBS has failed to provide adequate results in many cases due to sample matrix complexity, and NIRS performance is harmed because of its lack of sensitivity and indirect correlation with inorganic elemental species. In this work, the performance of these two techniques are compared for the determination of micro- and macroelements in vegetable samples (Brachiaria forages) using multivariate regression. In addition, a data fusion scheme, in which spectral data sourced by NIRS is integrated with LIBS, is proposed to improve elemental content determination in those samples. The information of the molecular composition detected by NIR vibrational spectroscopy was consistently selected by recursive partial least squares to yield quantitative multivariate models for K, Ca, Mg, Mn and Fe in forage plants that are superior to models based on the use of individual NIRS and LIBS spectral information. While all data fusion models showed better predictive accuracy than any of the two individual techniques, best results were observed for Ca. This suggests that matrix composition affects each element determination by LIBS distinctively and supports the idea that a successful quantitative data fusion strategy for LIBS requires a technique such as NIRS which is sensitive to this variability.

Qualitative analysis of the roots of Salvia miltiorrhiza and Salvia yunnanensis based on NIR, UHPLC and LC–MS-MS

Near infrared spectroscopy (NIR) was applied to discriminate the roots of salvia miltiorhiza Bunge (Danshen for short) and Salvia yunnanensis C. H. Wright (Zidanshen for short) by means of principal component analysis (PCA), improved and simplified K nearest neighbors (IS-KNN). Furthermore, an ultra-high performance liquid chromatographic (UHPLC) coupled with photodiode-array detector was developed for building fingerprints of lipophilic components of Danshen and Zidanshen, respectively. Basing on NIR information, both PCA and IS-KNN method classified the two kinds of Chinese medical herbs with 100% accuracy. The chromatographic fingerprints of the lipophilic components of Danshen and Zidanshen have 10 and 12 common peaks, respectively. Liquid chromatography coupled with mass spectroscopy (LC–MS-MS) was applied to identify these peaks. Among these, three small peaks in the fingerprints of Zidanshen are not found in Danshen, one of which was identified as α-lapachone, and the other two compounds were not yet identified; a small peak after tanshinone IIA in the fingerprints of Danshen was not found in Zidanshen, which was identified as miltirone. The two herbs have 10 common lipophilic components. The similarity between the two reference chromatograms of Zidanshen and Danshen is 0.902, but the mean similaritie between Zidanshen (or Danshen) fingerprints and its own reference chromatogram is 0.973 (or 0.976). The contents of main lipophilic components are significantly lower in Zidanshen than in Danshen (P < 0.01 or P < 0.05). The results indicate that the two Chinese medical materials are not only different in NIR spectra, but also different in species and quantities of lipophilic components. NIR spectra analysis can identify Danshen and Zidanshen rapidly and accurately. UHPLC coupled with MS analysis demonstrates the detail differences between the two herbs both in species and contents of their lipophilic components.

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

A hybrid approach for bioprocess state estimation using NIR spectroscopy and a sigma-point Kalman filter

Typically available online measurements in cultivations do not yield enough information to build up an optimization-based control for which model-based estimation techniques are necessary. More specifically, real-time information about the concentrations of substrates and biomass is the key to controlling and optimizing cultivations in a bioreactor. In addition to established techniques such as the measurement of dissolved oxygen, off-gas composition, and the amount of spent correction fluids for pH control, inline near-infrared spectroscopy (NIR) offers the possibility of gathering information online to estimate those concentrations, without the need for manual sampling. The NIR data can be transformed via the widely applied partial least squares (PLS) regression to estimate substrate and biomass concentrations. However, the spectra are corrupted by disturbances, such as gas bubbles from sparging, that can result in noisy and defective spectra and thus may lead to highly imprecise measurements. For feedback control, such imprecisions can lead to wrong decisions with respect to feeding rates, which may be hard to compensate for at a later stage. Moreover, information about operating conditions and physical constraints, such as feeding profiles and kinetic relations between substrates that could be used to rectify erroneous predictions, are disregarded in the PLS model. In this paper, we therefore present a hybrid approach of NIR spectroscopy and nonlinear, model-based state estimation to enable improved quality in the online estimation of substrates and biomass in aerated yeast cultivations. The feeding rates, off-gas concentrations, NIR spectra, and knowledge about the dynamics of the process are integrated seamlessly in a sigma-point Kalman filter (SPKF). The latter is formulated such that it is able to handle physical boundaries, which is especially important for fed-batch cultivations. In order to evaluate the hybrid approach, it is compared to different scenarios typically applied in fermentations. One uses only NIR data with a PLS regression model. The other works with an SPKF, but without NIR information. It is shown that the presented hybrid method outperforms the methods applied separately for the estimation of biomass, glucose, ethanol, ammonium, and phosphate in cultivations of a new, genetically modified strain of Saccharomyces cerevisiae. As only a few cultivations are needed for calibration, a reliable online state estimation is available in the early stage of development of this process.

Muscle Oximetry in Sports Science: A Systematic Review.

Since the introduction (in 2006) of commercially available portable wireless muscle oximeters, the use of muscle near-infrared spectroscopy (NIRS) technology is gaining in popularity as an application to observe changes in muscle metabolism and muscle oxygenation during and after exercise or training interventions in both laboratory and applied sports settings. The objectives of this systematic review were to highlight the application of muscle oximetry in evaluating oxidative skeletal muscle performance to sport activities and emphasize how this technology has been applied to exercise and training. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in a systematic fashion to search, assess and synthesize existing literature on this topic. The Scopus and MEDLINE/PubMed electronic databases were searched to 1 March 2017. Potential inclusions were screened against eligibility criteria relating to recreationally trained to elite athletes, with or without training programs, who must have assessed physiological variables monitored by commercial oximeters or NIRS instrumentation. Of the 14,609 identified records, only 57 studies met the eligibility criteria. This systematic review highlighted a number of key findings in 16 sporting activities. Overall, NIRS information can be used as a marker of skeletal muscle oxidative capacity and for analyzing muscle performance factors. Although NIRS instrumentation is promising in evaluating oxidative skeletal muscle performance when used in sport settings, there is still the need for further instrumental development and randomized/longitudinal trials to support the detailed advantages of muscle oximetry utilization in sports science.

Combined surface electromyography, near-infrared spectroscopy and acceleration recordings of muscle contraction: The effect of motion

Noninvasive techniques, surface electromyography (sEMG) in particular, are being increasingly employed for assessing muscle activity. In these studies, local oxygen consumption and muscle metabolism are of great interest. Measurements can be performed noninvasively using optics-based methods such as near-infrared spectroscopy (NIRS). By combining energy consumption data provided by NIRS with muscle level activation data from sEMG, we may gain an insight into the metabolic and functional characteristics of muscle tissue. However, muscle motion may induce artifacts into EMG and NIRS. Thus, the inclusion of simultaneous motion measurements using accelerometers (ACMs) enhances possibilities to perceive the effects of motion on NIRS and EMG signals. This paper reviews the current state of noninvasive EMG and NIRS-based methods used to study muscle function. In addition, we built a combined sEMG/NIRS/ACM sensor to perform simultaneous measurements for static and dynamic exercises of a biceps brachii muscle. Further, we discuss the effect of muscle motion in response of NIRS and EMG when measured noninvasively. Based on our preliminary studies, both NIRS and EMG supply specific information on muscle activation, but their signal responses also showed similarities with acceleration signals which, in this case, were supposed to be solely sensitive to motions.

Tracing sediment sources in a subtropical rural catchment of southern Brazil by using geochemical tracers and near-infrared spectroscopy

Conventional fingerprinting methods based on geochemical composition still require a time-consuming and critical preliminary sample preparation. Thus, fingerprinting characteristics that can be measured in a rapid and cheap way requiring a minimal sample preparation, such as spectroscopy methods, can be a good choice for this purpose. The present study aimed to evaluate the sediment sources contribution in a rural catchment by using conventional method based on geochemical composition and an alternative method based on near-infrared diffuse reflectance (NIR) spectroscopy. This study was carried out in a rural catchment with an area of 1.19km2 located in southern Brazil. The sediment sources evaluated were crop fields, unpaved roads and stream channels. Twenty nine suspended sediment samples were collected from nine significant storm runoff events between October 2009 and July 2011. NIR spectra of the sources and the suspended sediment samples were very similar. Nevertheless, the alternative method used to trace suspended sediment based on NIR spectroscopy was sensitive enough to detect differences in mineralogical composition of the sources materials, which were confirmed by X-ray diffraction analyses. Both conventional and alternative methods were able to discriminate the sediment sources and, despite the lack of correlation between the two methods, provided very close source ascription. The differences in the final results obtained by conventional and alternative methods are due to the nature of the variables used in each case, which are not mandatorily correlated. Efforts should be taken to try to combine both geochemical composition and near-infrared spectroscopy information on a single estimative of the sediment sources contribution in order to provide results with an even higher accuracy.

Cerebral and tissue oximetry.

The use of near-infrared spectroscopy (NIRS) has been increasingly adopted in cardiac surgery to measure regional cerebral oxygen saturation. This method takes advantage of the fact that light in the near-infrared spectrum penetrates tissue, including bone and muscle. Sensors are placed at fixed distances from a light emitter, and algorithms subtract superficial light absorption from deep absorption to provide an index of tissue oxygenation. Although the popularity of NIRS monitoring is growing, definitive data that prove outcome benefits with its use remain sparse. Therefore, widespread, routine use of NIRS as a standard-of-care monitor cannot be recommended at present. Recent investigations have focused on the use of NIRS in subgroups that may benefit from NIRS monitoring, such as pediatric patients. Furthermore, a novel application of processed NIRS information for monitoring cerebral autoregulation and tissue oxygenation (e.g., kidneys and the gut) is promising.

Survey of the Clinical Assessment and Utility of Near-Infrared Cerebral Oximetry in Cardiac Surgery

Near-infrared cerebral oximetry increasingly is used for monitoring during cardiac surgery. Nonetheless, the scientific basis for incorporating this technology into clinical practice, the indications for when to do so, and standard diagnostic and treatment algorithms for defining abnormal values are yet to be rigorously defined. The authors hypothesized that there would be (1) variation in clinical use and practices for near-infrared spectroscopy (NIRS), and (2) variation in management of patients when clinicians are provided with NIRS information. In order to test this hypothesis, they sought to assess the nature and strength of response heterogeneity among anesthesiologists and cardiac perfusionists when provided with cardiac surgery patient scenarios and cerebral oximetry data. A prospectively collected survey. A hospital-based, multi-institutional, multinational study. By e-mail, the authors surveyed the membership of the Society of Cardiovascular Anesthesiologists and the online Cardiovascular Perfusion Forum. This survey was focused on ascertaining what actions clinicians would take in each scenario, given case information and cerebral oximetry tracings. Questions were based on 11 patient scenarios selected to represent small, large, symmetric, or asymmetric decreases in measured regional cerebral oxygen saturation (rScO2) encountered during cardiac surgery. Information on the respondents' (n = 796; 73% anesthesiologists) clinical practice, demography, and cerebral oximetry utilization was collected. An index of dispersion was used to assess response heterogeneity overall and within demographic subgroups. The majority of respondents indicated that cerebral oximetry monitoring was either useful or an essential monitor, especially perfusionists and clinicians who used cerebral oximetry most frequently. There were marked differences in responses between perfusionists and anesthesiologists for 4 of the 6 scenarios (p<0.005 for each of these 4 scenarios) occurring during cardiopulmonary bypass. Scenarios having greatest rScO2 reduction or asymmetry in rScO2 were associated with the highest dispersion, indicating least agreement in management. Scenarios with mild or moderate rScO2 reduction were associated with the lowest dispersion, indicating greater agreement in management. Although experimental data gradually are accumulating to support the role for cerebral oximetry monitoring during cardiac surgery, the results of the present survey support the view that its role remains poorly defined, and consensus for its appropriate use is lacking. Importantly, the authors observed marked variation in the use, perceived utility, and management of patients for 4 of the 6 CPB scenarios between perfusionists and anesthesiologists who share the management of CPB. These findings support the need for well-designed, adequately-powered clinical trials examining the value of this technology.

Near-infrared spectroscopy in acute compartment syndrome: Case report

A 44-year-old Caucasian male was treated with external fixation to stabilise a proximal tibia fracture with an articular split. Postoperatively, the patient became hypersensitive to pain in both the injured and uninjured legs. Intramuscular pressures (IMPs) were measured in all four compartments of the injured extremity resulting in 40-mmHg perfusion pressure in all four compartments. (Perfusion pressure was defined as diastolic blood pressure minus IMP.) Tissue oxygenation was measured using two near-infrared spectroscopy (NIRS) pads (INVOS, Somanetics, Troy, MI, USA). NIRS pads were placed on the lateral and deep posterior compartments for continual monitoring as described by Shuler et al. (All clinical decisions were based on the clinical symptoms and pressure measurements and not on the NIRS information.) Repeat IMPs were obtained 2 h later and confirmed adequate perfusion gradients of at least 40 mmHg in all four compartments. Approximately 24 h after the initial injury and 18 h after external fixation, the patient began requiring more pain medication. IMP measurements were repeated a third time. The perfusion gradients for the posterior compartments were 11 mmHg, whilst the anterior and lateral were maintained over 20 mmHg of perfusion gradient. A four-compartment fasciotomy was performed. During the release, no gross evidence of muscle necrosis was found.

New prototype NIRS instrument [corrected] to investigate multi-regional cerebral blood and tissue oxygenation and haemodynamics.

Near infra-red spectroscopy (NIRS) is a non-invasive optical technique used to assess blood and tissue oxygenation and haemodynamics in living tissue. It is presently used as a semi-continuous monitoring technique on a research basis. Multi-regional NIRS information could be beneficial for a better understanding of the development of cerebral injuries and the reduction of neurodevelopmental abnormalities in the human newborn. The paper presents the conversion of the continuous wave NIRS single-channel instrument (NIRDCU) into a prototype four-channel instrument which can be used to provide continuous multi-regional information regarding the cerebral blood oxygenation and haemodynamics in the frontal, left parietal, right parietal and occipital region of the neonatal brain. This has been achieved by attaching an optical multiplexing system to the NIRDCU to select different transmitting and receiving fibres, thus addressing different regions in the brain. Preliminary laboratory tests on a phantom and on the adult forearm are presented, illustrating the practicality of regional monitoring.