Potential Of Near-infrared Spectroscopy Research Articles

Enhancing quality control in emulsion-type sausage production: Predicting chemical composition of intact samples with near infrared spectroscopy

This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.

Near-Infrared (NIR) Spectroscopy as an Alternative for Predicting n-Alkane Concentration in Excreta of Laying Hens: NIR-Generated Data for Dietary Composition Estimation.

N-alkanes offer a promising approach for assessing the nutritional contribution of external sources to the diets of laying hens in free-range production systems. However, traditional laboratory methods, involving extraction, purification and gas chromatographic analysis, are both economically burdensome and time-consuming. Near-infrared spectroscopy (NIRS) is emerging as a viable alternative, with varying degrees of accuracy depending on the chemical nature and concentration of the component of interest. In our research, we focus on the accuracy of NIRS in predicting the concentrations of n-alkanes (C25-C33) in excreta under simulated free-range conditions with two different diets: one containing a commercial feed with minimal n-alkane content and another containing 1% alfalfa on top of the commercial feed. Spectra processing and calibration were tailored for each n-alkane, with NIRS performance influenced by diet type. Notably, plant predictions using NIR-generated data were consistent with laboratory results, despite a slight tendency toward overestimation (3.40% using the NIRS-generated C25-C29-C33 combination versus 2.80% using laboratory analysis). This indicates the potential of NIRS as an efficient tool to assess n-alkanes in excreta of laying hens and, consequently, the nutritional contribution of the free-range environment, providing rapid and cost-effective results.

Near-Infrared spectroscopy combined with machine learning methods for distinguishment of the storage years of rice

Rice is one of the most important food crops that provide essential nutrients, micronutrients and daily energy for humans. The freshness of rice determines the quality and nutrition supply property, but the freshness decreases along with the storage time. A simple, nondestructive and rapid detection technology is needed to estimate the time of storage rice as for a fast evaluation of the rice quality. To accomplish this objective, near-infrared spectroscopy (NIRS) is employed in combination with three machine learning methods, including least square support vector machine (LSSVM), random forest (RF) and principal component-neural network (PC-NN). With specific design on grid search of the relevant parameters, the LSSVM model optimally performed classification with the highest accuracy of 95.7% in the distinguishment of three labeled storage years, the RF model and PC-NN models have close accuracies in model training and optimization processes. In comparison to the PLS method, which is the typical chemometric method in NIRS data analysis, the three presented machine learning methods all perform excellent over the PLS model for model training and for model testing. Especially the RF and PC-NN model were optimized by hyperparameter training, to obtain 90% of testing accuracy and reduced the error differences to ∼5.0% between model training and testing. This study indicated the potential of NIRS in combination with machine learning methods as practical chemometric tools for discrimination of the rice storage freshness by distinguishing their storage years. The design of adaptive tuning on hyperparameters provide a valuable approach to improve the model prediction abilities.

Hemodynamic alterations response to Chinese acupuncture therapy monitored by a custom near-infrared spectroscopy probe with an open hole.

Traditional acupuncture, a popular traditional Chinese medicine (TCM) technique, has demonstrated potential in relieving various ailments' symptoms. However, its black-box feedback loop model has limited proper evaluation and use by unskilled practitioners. This study aimed to analyze hemodynamic signals around acupoints during acupuncture to identify significant parameter changes. We designed hollow near-infrared spectroscopy (NIRS) probes for real-time measurements during acupuncture at acupoints, monitoring oxy-hemoglobin (HbO), deoxy-hemoglobin (Hb), and blood volume (HbT) changes. Acupuncture was performed on the "Xuehai" acupoint in 17 healthy subjects, with NIRS measuring hemodynamic alterations. Results showed significant and consistent increases in Hb and HbT around the acupoint, returning to baseline values after needle removal. One case of fainting revealed a significant Hb increase and HbO decrease. Acupuncture may induce tissue vasodilation and enhance oxygen consumption. This research provides a potential explanation for acupuncture's mechanism and emphasizes NIRS's potential in TCM.

Espectroscopia no infravermelho próximo (NIRS) em unidade de terapia intensiva neonatal: experiência de um hospital universitário brasileiro

Near-infrared spectroscopy (NIRS) is a non-invasive method to assess regional brain and/or renal oxygenation, and an important tool for a better clinical management. The aim of this study was to describe clinical cases of newborns admitted to the Neonatal Intensive Care Unit of a Brazilian university hospital who were monitored with NIRS, and to evaluate the applicability of NIRS demonstrating its use as a continuous monitoring method for newborns at high risk for brain injury. This study was part of the Conclusion Work of Medical Residency in Neonatology in Neonatology. Case 1: Persistent ductus arteriosus-NIRS guided indication for drug treatment. Case 2: Septic shock-NIRS was helpful identifying the need of inotropes. Case 3: Anemia of prematurity-NIRS showed an increase of cerebral oxygenation after red blood cells transfusions. Case 4: Hypoxic-ischemic encephalopathy-NIRS identified sign of poor neurological prognostic. NIRS is a continuous cerebral and renal perfusion monitoring method that has easy and good applicability and no complications. The cases reflect the importance and potential of NIRS in clinical practice and in helping diagnose for newborns, as well as enabling prognostic assessment. NIRS proved to be a method of good applicability, without short-term complications. Observing brain oxygenation values can help to avoid conditions associated with the development of neurological sequelae.

First Report of the Detection of DENV1 in Human Blood Plasma with Near-Infrared Spectroscopy.

Dengue virus (DENV) is the world’s most common arboviral infection, with an estimated 3.9 million people at risk of the infection, 100 million symptomatic cases and 10,000 deaths per year. Current diagnosis for DENV includes the use of molecular methods, such as polymerase chain reaction, which can be costly for routine use. The near-infrared spectroscopy (NIR) technique is a high throughput technique that involves shining a beam of infrared light on a biological sample, collecting a reflectance spectrum, and using machine learning algorithms to develop predictive algorithms. Here, we used NIR to detect DENV1 artificially introduced into whole blood, plasma, and serum collected from human donors. Machine learning algorithms were developed using artificial neural networks (ANN) and the resultant models were used to predict independent samples. DENV in plasma samples was detected with an overall accuracy, sensitivity, and specificity of 90% (N = 56), 88.5% (N = 28) and 92.3% (N = 28), respectively. However, a predictive sensitivity of 33.3% (N = 16) and 80% (N = 10) and specificity of 46.7% (N = 16) and 32% (N = 10) was achieved for detecting DENV1 in whole blood and serum samples, respectively. DENV1 peaks observed at 812 nm and 819 nm represent C-H stretch, peaks at 1130–1142 nm are related to methyl group and peaks at 2127 nm are related to saturated fatty groups. Our findings indicate the potential of NIR as a diagnostic tool for DENV, however, further work is recommended to assess its sensitivity for detecting DENV in people naturally infected with the virus and to determine its capacity to differentiate DENV serotypes and other arboviruses.

II-2. Potential of near-infrared spectroscopy in quality evaluation of seafood

II-2. Potential of near-infrared spectroscopy in quality evaluation of seafood

Near infrared spectroscopy as a tool for agricultural expertise: identification of tomato seedlings

Tomatoes are one of the most prominent vegetables globally, with significant cultural and economic relevance in various nations, including Brazil. The term ‘safe food’ is becoming more popular as consumer preferences and supply chain dynamics become evolved in these processes. In light of these issues, the use of safety and quality management methods for fruits and vegetables have increased dramatically, with traceability being one of these solutions worth highlighting. When it comes to traceability, evaluation of tomato seedlings, plants, and fruits to identify groups or hybrids becomes particularly crucial throughout the marketing process, since the consumer of seedlings or fruit has difficulties recognizing whether that product truly belongs to the group indicated by the merchant. Thus, the potential of near infrared spectroscopy (NIRS) combined with the PC-LDA and PLS-DA algorithms was tested for the discrimination of two significant commercial groups, Salada and Saladete, as well as eleven cultivars belonging to these groups, which were tested for this purpose. The results show that, by using the PLS-DA model, the portable NIR equipment is capable of differentiating tomato seedlings in nurseries of the Salada and Saladete groups, with an accuracy of 99.7% and sensitivity of 100%. The technique showed to be efficient for individual models of tomato seedlings in the Salada group, with accuracy over 90% and sensitivity above 93% for all models. For the Saladete group's individual models, the technique proved effectiveness for the hybrids Parma, BS-110012, Giácomo, Guara, and Tyna.

Near infrared spectroscopy as a fast and non-destructive technique for total acidity prediction of intact mango: Comparison among regression approaches

In the present study, the potential of near infrared spectroscopy (NIRS) as a rapid and non-destructive tools for quality attributes measurement of intact mango was investigated. Three different regression approaches namely partial least square regression (PLSR), support vector machine regression (SVMR), and artificial neural network (ANN) were used and compared in predicting total acidity (TA) of intact mangos. This quality parameter prediction models were established based on near infrared diffuse reflectance spectra acquired in wavelength range from 1000 to 2500 nm. Standard normal variate (SNV) transformation was applied as spectra enhancement prior to prediction models development. The results obtained show that ANN and SVMR are better than PLSR for TA prediction. The optimal prediction model for TA quality attribute were obtained by ANN with the first 4 principal components (PCs) scores as input. The coefficient determination of calibration (R2cal) and prediction (R2pred), the root-mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation (RPD) were 0.97, 0.89, 25.29 mg100g−1, 28.42 mg100g−1 and 4.02, respectively. The overall results satisfactorily demonstrate that NIRS technology combined with proper regression approaches has the promising results to determine TA of intact mango non-destructively.

Detection of spongy pulp in guava using light properties and near infrared spectroscopy

Spongy pulp defect in guava is recognised by a dry-looking pulp with a brown colour, and not acceptable to consumers which causes substantial loss in value. Current detection of spongy pulp uses visual assessment of the flesh, which is half cut from the sample. The present work aimed to develop a classifying model based on non-destructive technique for the detection of spongy guava. Guava samples harvested at full maturity were determined for visible light properties, visible light reflectance, and near infrared reflectance. The light properties and light reflectance of guava peel were used to derive a classification model which was then compared with a near infrared reflectance model, which in turn provided absorbance of the flesh and peel using stepwise discriminant analysis. The models were used to classify the guavas into normal and spongy flesh groups, which were assigned with reference to the visual assessment on half cut samples. The classification accuracy for the model using gloss and light reflectance at 650 nm (chlorophyll b) was 90.4%. However, the model developed from the near infrared absorbance provided better accuracy (92.7%). It appeared that the largest wavenumber at 4,721 cm-1 contributed to the total sugar content, which implied that spongy and normal guavas had different total sugar contents in the flesh. The present work demonstrated the potential of near infrared spectroscopy to discriminate spongy from normal guavas. However, the accuracy of the classification could be further improved by analysing more samples from the next season.

Effects of cool resistance exercise on oxygen metabolism in muscle tissue and clinical application

Resistance training is beneficial for elite athletes and the elderly, alike. It plays a key role in healthy ageing, helping retain muscle mass, and it can also help reduce the chances of chronic diseases such as osteoporosis and high blood pressure. In order for resistance exercise to be effective, there is an ongoing need for proven techniques that can assist with their resistance training. Professor Masahiro Goto, Director of the Department of Physical Therapy, Aino University, Japan, works to understand the physiological dynamics at play during resistance exercise with a view to developing novel exercise methods and using new technologies to improve resistance exercise outcomes. A brief intramuscular hypoxic state is key to promoting muscle growth and this has therefore been a target for improving resistance techniques, but a challenge arises in terms of monitoring hypoxia. That is why Goto is looking to establish a method that can accurately, quickly and cheaply test the intramuscular oxygenation state during resistance exercise. Recognising the potential of near infrared spectroscopy (NIRS), he performed a study on volunteers who have resistance exercise carrier more than one year over eight weeks to find out whether NIRS can be used as an effective measure of internal muscular oxygenation levels and if cold pack treatment of muscles prior to exercise results in better muscle mass gain. He used electromyograms to measure muscle contraction, ultrasound to monitor changes in muscle mass and arterial oxygen saturation measurements to elucidate oxygenation levels. He found that NIRS was effective for routine monitoring of oxygen levels and that cold pack treatment during resistance exercise causes higher muscle activity and creates a more hypoxic muscular environment than without any cold treatment.

The potential of NIR spectroscopy in the separation of plastics for pyrolysis

The present-day substantial growth in the demand and utilization of plastics provokes severe economic and environmental consequences. Around 4 – 6 % of global oil and gas production is used directly or indirectly as feedstock in the production of plastics. A further 2 – 3 % is employed as energy during the manufacturing process. This study highlights recycling (chemical) against other sustainable waste management approaches, like mitigation of waste generation through the concept of reusing and energy recovery from plastics. As an example, the African context regarding the quality of the disposed waste and the waste characteristics in the Kumasi region, Ghana is taken into consideration. To process valuable and economically viable recycling products, pure polymers are required. Certain technologies, such as infrared (IR) spectroscopy, have limitations for accurately identifying different polymer types, particularly when the sample mix is contaminated with organic waste or is physically wet. There are promising technologies that are under development, like Raman spectroscopy and laser-aided spectroscopy combined with tracers (fluorescent markers). Nonetheless, these are essentially more expensive technologies and are currently in the development phase. Multiplexed near-infrared (NIR) spectroscopy is a fitting technology for polymer identification. It is a fast and nondestructive technique that does not influence the physical state nor chemical property of the sample polymers. Hence, it can be integrated in a continuous sorting system. Here, a prototype sorting system equipped with a multiplexed NIR spectrometer was utilized and used to test the sorting efficiency of the system as well as the purity of identified and sorted samples. Samples of PE (with subgroups of HDPE, LDPE and LLDPE), PS, PET, PP and PVC and unknown polymers were employed in several conditions. The measurements were carried out in real time, based on the speed of the conveyor belt. In this study, a novel setup is introduced and investigated, and its data analyzed to determine the reliability of the sorting method for plastics to be used for pyrolysis.

Light at the museum – A near impossible result

The monitoring and quantification of the illegal harvest of protected animal products is very vital for the conservation and protection of endangered species. Most of the methods and techniques used in the trade of these products are recognised to be incredibly time consuming and labour intensive requiring significant analyst expertise. In this study, we have demonstrated the potential of near-infrared spectroscopy combined with either principal component analysis or partial least square discriminant analysis regression as a rapid and non-invasive tool to classify horn and ivory samples stored in the Australian Museum, Sydney. This study has also demonstrated the attractiveness of the near-infrared technique as a screening tool that could revolutionise the tracking and identification of contraband materials produced from horn and ivory biomaterials.

Rapid and Green Method Forensic Authentication of Rice Using Near-Infrared Spectroscopy (NIRS)

Rice is one of the most consumed cereals in the world. Currently, techniques for the authentication and geographical origin of rice is known not to be objective because to depend on the naked eye of a well-trained inspector. DNA fingerprint methods have been shown to be inappropriate for on-site application because the method needs a lot of labor and skilled expertise. Rice consumers want to confirm cultivation origin because they believe price or eating score has a high correlation according to them. Considering rice as a raw material of economic and social value and the recent use of NIR spectroscopy coupled with chemometric methods to authentication and discrimination of geographical origin as an alternative to classical methods in the search for a methodology in line with Green Chemistry, this work investigates the potential of NIR spectroscopy combined with multivariate analysis: PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis) for rapid and non-destructive forensic authentication of rice grains from Brazil and Venezuela. This study investigated the potential of near-infrared spectroscopy, combined with PCA and HCA chemometric technique to the authenticity of rice. It was verified that is feasible and advantageous to implement authenticity detection of different brands, typology and geographical discrimination (Brazil and Venezuela) rice.

Chemical Characterization of Wine Vinegars Belonging to the Vinagre de Montilla-Moriles Protected Designation of Origin, Using Near-Infrared Spectroscopy

Spanish wine vinegars belonging to Vinagre de Montilla-Moriles protected designation of origin (PDO) must satisfy some chemical characteristics. These characteristics are mainly responsible of their high and exceptional quality. This study assessed the potential of near-infrared spectroscopy (NIRS) as a non-destructive technology for characterizing wine vinegars belonging to this PDO. A total of 107 vinegars were used to predict major chemical quality parameters (volumic mass, reducing sugars, total acidity and pH) using a scanning monochromator (spectral range 400–2500 nm) with the spinning module, working in transflectance mode. The models developed showed values for the coefficient of regression for cross-validation between 0.95 and 0.99 for volumic mass, reducing sugars and total acidity. Therefore, the results confirm that NIRS technology combined with linear regression strategies such as the modified partial least squares (MPLS) regression can indeed respond to the needs of the vinegar cellars and help them to measure the commonest chemical quality parameters of wine vinegars belonging to Vinagre de Montilla-Moriles (PDO), especially in the case of vinegars with different sugar contents (dry, semi-sweet, sweet and balsamic). However, the number of vinegars as well as their variability should be increased in order to obtain more robust models.

Potential of Near-Infrared Spectroscopy to Evaluate the Cleaning Performance of Dishwashing Processes

Abstract Automatic dishwashing is a process which takes enormous amounts of resources (energy, water, chemicals). However, most of the dishwashing programs are designed and tested to clean the worst scenario for a certain kind of load items (glass, porcelain, etc.) and do not or only partly adapt to the actual load and soiling conditions. Thus, resources are wasted which could be avoided if a better adaptation to the actual status of the dishwashing process would be possible. It is, therefore, the aim of this study to investigate the possibility of using near-infrared technology to evaluate the status of the dishwashing process by analyzing the content of soil components already dissolved in the dishwashing water. Compared to other methods, the main advantage of near-infrared measurements is the possibility to differentiate between soils or soil components (carbohydrates, fats and protein) at very low concentrations. Near-infrared spectroscopy implemented in an automatic dishwasher could observe and evaluate an automatic dishwashing processes by using online measurements of the detachment of soils from soiled dishes. Thirty-seven model emulsions containing detergent, tap water, different soils and soil concentrations were created and measured by using near-infrared spectroscopy to demonstrate its suitability for the evaluation of dishwashing processes. The spectra were the basis of developing near-infrared calibration models for each soil component using the partial least squares regression method with internal cross-validation. The coefficients of determination in internal cross-validation (R²) are 0.88 for carbohydrates, 0.92 for fats and 0.99 for proteins. Thereafter, the calibration models developed were externally validated with samples of known concentrations of these soil components. The known concentrations were determined during external validation. The results of calibration and evaluation show that it is generally possible to measure very low concentrations of soil components in dishwashing liquor. The applicability to a real dishwashing process and the usage of other soils has yet to be tested.

Near-Infrared Spectroscopy to Monitor Nutritional Status of Neonates: A Review.

The World Health Organization reported that half or more of all under five deaths were caused by undernutrition in developing countries, with the majority of these deaths occurring in the first week of life. Even if the undernourished neonates manage to survive, they are exposed to long-term health impacts, including obesity, cardiovascular disease, and hypertension. Along with those health-impacts they can be exposed to risks related to detrimental early development, such as physical impairment, stunting, brain dysfunction, and reduced cognitive development. Body fat percentage has been recognized to be closely associated with undernutrition in neonates. In this article, the potential of near infrared spectroscopy (NIRS), along with previous methods to measure body fat in neonates, is reviewed and discussed.

The Use of Near-Infrared Spectroscopy for the Prediction of Gaseous and Particulate Emissions from Agricultural Feedstock Pellets

The potential of near-infrared spectroscopy in conjunction with chemometric techniques to predict the particulate matter and gaseous emissions of biomass pellet blends was assessed in this study. A diverse range of biomass was used, including wood, Miscanthus, wheat straw, and the herbaceous energy grass Szarvasi-1 (Elymus elongatus subsp. ponticus cv. Szarvasi-1). The particulate matter emissions were predicted with root-mean-square errors of prediction (RMSEP) of 6.83 (R2 = 0.57), 8.71 (R2 = 0.66), and 11.25 (R2 = 0.65) mg m–3 for the PM10, PM0, and TSP emissions, respectively. The gaseous emissions of oxides of nitrogen (NOx), sulfur dioxide (SO2), and carbon monoxide (CO) were predicted with RMSEPs of 14.28 (R2 = 0.93), 4.59 (R2 = 0.88), and 9.08 (R2 = 0.48) mg m–3, respectively. No significant models could be developed for the PM2.5 or PM1 emissions. The results indicate that near-infrared spectroscopy has the potential to predict the emissions of biomass pellets in a multibiomass stream.

Discrimination of vegetable oil types using Fourier transforms near infrared spectroscopy coupled with pattern recognition techniques

The aim of this research was to investigate the potential of near infrared spectroscopy (NIRs) coupled with pattern recognition techniques for discriminating of vegetable oil types (i.e. coconut oil, olive oil, rice bran oil, sesame oil, soybean oil and sun flower oil). Principle component analysis (PCA) was performed for clustering vegetable oil types. Five of supervised pattern recognition techniques such as soft independent modelling of class analogies (SIMCA), Partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) were used to identify vegetable oil types. The PCA model could separate coconut oil from other vegetable oils. Two PLS-DA and SVM models showed 100% of precision, recall F-measure and accuracy for all vegetable oil whilst remainder techniques achieved a satisfactory classified performance. All supervised models could discriminate coconut oil from other oils with precision, recall F-Measure and accuracy of 100%. It seems that NIRs technique coupled with pattern recognition techniques is possible for discriminating vegetable oil types.

Non-destructive screening method for detecting the presence of insects in sorghum grains using near infrared spectroscopy and discriminant analysis

The potential of near-infrared spectroscopy (NIRS) combined with partial least squares discriminant analysis (PLS-DA) to develop a screening method for distinguishing uninfested from infested sorghum (Sorghum bicolour (L.) Moench) grains was for the first time investigated. A total of 108 sorghum grain samples from thirty-six different genotypes were infested with seventy Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) unsexed adult insects during seventy days. More 101 uninfested sorghum grain samples from these same genotypes were used to build models. Principal components analysis (PCA) allowed slightly discriminating between the two classes along principal component two. A PLS-DA model presented perfect classification rates, with sensitivity and specificity equal to 100% for the test set. In addition, the model showed high accuracy, and accordance and concordance (precision) both equal to 100%. These results showed that the combination of NIRS with PLS-DA provided a rapid, cost-effective and non-invasive way to detect insect infestation in sorghum grains.