Non-destructive Near Infrared Research Articles

Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

AbstractIn Australia, the soil‐borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near‐infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA‐SVM) for early‐stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA‐SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non‐inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions.

Non-destructive detection of polysaccharides and moisture in Ganoderma lucidum using near-infrared spectroscopy and machine learning algorithm

Ganoderma lucidum, the fruiting body of the Poraceae fungi G. lucidum or Ganoderma sinense, has a long history of use in promoting health and longevity in Asian countries. However, traditional methods for detecting polysaccharides and moisture in G. lucidum are complicated, time-consuming, and damaging (to the sample). In this study, rapid and nondestructive near-infrared (NIR) spectroscopy (700–2500 nm) was uesd to directly scan the back of the G. lucidum cap without powdering. Thereafter, we used synergy interval partial least squares to select the performing band and the ant lion optimization (ALO) algorithm to optimize the least squares support vector machine (LSSVM) model for these two components. The results showed that the ALO-LSSVM model could predict the total polysaccharide and moisture content with high accuracy. The correlation coefficient for calibration were both >0.9 and their ratio of prediction to deviation (RPD) values of prediction were 2.6 and 3.6, respectively, indicating the non-destructive determination of polysaccharides and moisture in G. lucidum will provide great convenience for on-site testing by procurement personnel. This shows the combination of NIR spectroscopy and the ALO-LSSVM algorithm has potential applications for the rapid and nondestructive analysis of natural products.

Near-Infrared Metabolic Profiling for Discrimination of Apricot and Peach Kernels.

Apricot and Peach Kernels are commercial crude drugs used in many formulas of traditional Japanese medicine, Kampo. Although their applications are quite different, it is difficult to distinguish them using conventional methods such as HPLC. The study aimed at near-infrared (NIR) metabolic profiling to discriminate Apricot and Peach Kernels (Armeniacae Semen and Persicae Semen) collected from Japanese markets. A fast, simple, non-destructive, and robust NIR measurement of kernel surface with no sample pre-treatment was achieved in situ. Principal component analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) models showed discrimination between the two crude drugs with good fitting and prediction values. These results indicate that NIR metabolic profiling is useful for discriminating Apricot and Peach Kernels based on their chemical constituents using a simple and non-destructive procedure.

Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression

Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.

Printed graphene-based electrochemical sensor with integrated paper microfluidics for rapid lidocaine detection in blood

Topical lidocaine patches are commonly used to relieve pain and suffering in various clinical and household settings. Despite its extensive use, excessive skin absorption during numbing or pain reduction procedures can cause systemic toxicity, which can lead to life-threatening conditions. Rapid and reliable monitoring of escalating levels of lidocaine in the blood could help management/prevention of lidocaine overdose and its associated complications. To address this need, here we have developed a disposable point-of-care (POC) diagnostic platform composed of an integrated graphene-based electrochemical sensor with paper-based microfluidics for rapid detection of lidocaine levels in serum and blood samples. The fabrication process takes advantage of advanced, scalable manufacturing techniques, including printing, laser processing, and nondestructive near infrared (NIR) drying. The sensitivity tests of the platform revealed a sensitivity of ∼0.2 μA μM−1 towards lidocaine concentrations in the clinically relevant range (1–100 μM) in both complex matrix fluids of serum and blood with high cross specificity in the presence of the interfering analytes. This proof-of-concept platform could be regarded as the first step toward the development of low-cost and translational POC devices that could help in better pain management and reduce potential side effects or misuse of analgesics.

Evaluation of portable near-infrared spectroscopy for authentication of mRNA based COVID-19 vaccines.

Since its identification in 2019, Covid-19 has spread to become a global pandemic. Until now, vaccination in its different forms proves to be the most effective measure to control the outbreak and lower the burden of the disease on healthcare systems. This arena has become a prime target to criminal networks that spread counterfeit Covid-19 vaccines across the supply chain mainly for profit. Counterfeit vaccines provide false sense of security to individuals, heightens the risk of exposure and outbreak of the virus, and increase the risk of harm linked to Covid-19 infection. Moreover, the increase in counterfeit vaccines feeds hesitancy towards vaccination and erodes the trust in mass immunisation programmes. It is therefore of paramount importance to work on rapid and reliable methods for vaccine authentication. Subsequently this work utilised a portable and non-destructive near infrared (NIR) spectroscopic method for authentication of Covid-19 vaccines. A total of 405 Covid-19 vaccines samples, alongside their main constituents, were measured as received through glass vials. Spectral quality and bands were inspected by considering the raw spectra of the vaccines. Authentication was explored by applying principal component analysis (PCA) to the multiplicative scatter correction-first derivative spectra. The results showed that NIR spectra of the vaccine featured mainly bands corresponding to the mRNA active ingredient. Fewer bands corresponded to the excipients and protein spectra. The vaccines NIR spectra were strongly absorbing with maximum absorbances up to 2.7 absorbance units and that differentiated them from samples containing normal saline only (constituent reported for counterfeit Covid-19 vaccines). Clustering based on PCA offered optimal authentication of Covid-19 vaccines when applied over the range of 9000–4000 cm-1These findings shed light on the potential of using NIR for analysing Covid-19 vaccines and presents a rapid and effective initial technique for Covid-19 vaccine authentication.

Near infrared spectroscopic assessment of engineered cartilage for implantation in a pre-clinical model

IntroductionArticular cartilage lacks the capacity for self-repair due to its relative avascularity. Regenerative medicine approaches are now widely investigated, but optimal solutions have not been identified. This is partially attributable to challenges in identifying appropriate engineered constructs for implantation, and in assessing the repair trajectory in pre-clinical models. Objective: We investigated non-destructive near-infrared (NIR) fiber optic spectroscopy-derived parameters to inform (1) identification of optimal tissue engineered cartilage constructs prior to implantation and (2) assessment of repair tissue composition in a Yucatan mini-pig chondral defect model. MethodsTissue engineered cartilage (TEC) was developed using a co-culture system of bone marrow mesenchymal stem cells and chondrocytes encapsulated in a hyaluronic acid-based hydrogel. Near-infrared spectroscopy (NIRS)data were obtained from the TECs pre- and post-implantation and from repair tissue post-surgery. Healing response of the defects were compared to microfracture at 3 months post-implantation based on ICRS II scoring, mechanical testing, and NIRS. ResultsNo significant differences were found between the tissue engineered cartilage and microfracture repair tissue. Some NIR-determined properties of the developing TECs correlated significantly with aspects of the ICRS II repair tissue score, including the surface architecture and subchondral bone integration. Further, NIR-derived metrics to non-destructively assess repair tissue composition, in the presence of subchondral bone, were defined (5200/7000 cm−1 ratio), providing a valuable approach for future studies. ConclusionTogether, these data demonstrate the utility of NIR spectroscopy to identify developing TECs that may result in functional repair tissue and to facilitate repair tissue evaluation.

Near-infrared spectroscopy and machine learning-based classification and calibration methods in detection and measurement of anionic surfactant in milk

The monitoring of milk and milk-based products are crucial in the dairy industry because of their global consumption, fraudulent practice applied to mask the quality parameters for gaining more profit. Non-destructive near-infrared (NIR) spectroscopy and machine learning (ML) methods like interval partial least squares (iPLS), principal components analysis (PCA), and soft independent modeling of class analogy (SIMCA) were used to classify and discriminate pure milk samples from adulterated with an anionic surfactant. The surfactant’s model was sodium dodecylbenzene sulfate (SDBS) in different concentrations (1.94–19.4 gkg−1). ML models were assessed by sensitivity, specificity and efficiency rates as classification figures of merit. As a result, iPLS regression method based on standard normal variate (SNV) pre-processed spectra provided better performance compared to partial least squares (PLS) regression with a full-spectrum dataset. The root mean square error of cross-validation (RMSECV) was 0.123 and 0.135 for the iPLS and full-spectrum models, respectively. PCA as a qualitative grouping analysis demonstrated separate clusters of samples in terms of their concentration level of SDBS. For the iPLS dataset in SIMCA model, all classification figures of merit were recorded 100 % for pure milk samples. In contrast, the average sensitivity, specificity and efficiency rates were 97.6 %, 93 %, and 94.9 % for adulterated samples.

ShiningNIRlight on ivory: A practical enforcement tool for elephant ivory identification

AbstractThe elephant ivory trade remains controversial because of concerns about the extinction risk of elephants and the different needs of CITES member states. Thailand's situation is particularly contentious because of the different legal status among types of elephant ivory. Thai law allows the local sale of ivory from domesticated Asian elephants, which creates challenges for Thai enforcement officers in identification of ivory provenance. We investigated the capacity of non‐destructive Near Infrared (NIR) spectroscopy (600–1700 nm), combined with Partial Least Squares Discriminant Analysis (PLS‐DA), to discriminate between ivory from African, wild Asian and domesticated Asian elephants. Ivory spectra of 64 elephants were divided randomly into calibration and validation datasets. We were able to determine elephant ivory provenance at both the interspecies (African and Asian elephant ivory), and within species (wild and domesticated Asian elephant ivory) classifications with 100% accuracy. These results showed the potential use of handheld NIR spectrometers for rapid assessments of ivory provenance, as well as a forensic tool for wider enforcement.

The use of near infrared spectroscopy to predict foliar nutrient levels of hydroponically grown teak seedlings

Due to a combination of durability, strength, and aesthetically pleasing color, teak ( Tectona grandis L.f.) is globally regarded as a premier timber species. High value, in combination with comprehensive harvesting restrictions from natural populations, has resulted in extensive teak plantation establishment throughout the tropics and subtropics. Plantations directly depend on the production of healthy seedlings. In order to assist growers in efficiently diagnosing teak seedling nutrient issues, a hydroponic nutrient study was conducted at North Carolina State University. The ability to accurately diagnose nutrient disorders prior to the onset of visual symptoms through the use of near infrared (NIR) technology will allow growers to potentially remedy seedling issues before irreversible damage is done. This research utilized two different near infrared (NIR) spectrometers to develop predictive foliar nutrient models for 13 nutrients and then compared the accuracy of the models between the devices. Destructive leaf sampling and laboratory grade NIR spectroscopy scanning was compared to nondestructive sampling coupled with a handheld NIR device used in a greenhouse. Using traditional wet lab foliar analysis results for calibration, nutrient prediction models for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), copper (Cu), molybdenum (Mo), magnesium (Mg), boron (B), calcium (Ca), manganese (Mn), iron (Fe), sodium (Na), and zinc (Z) were developed using both NIR devices. Models developed using both techniques were good for N, P, and K (R2 > 0.80), while the B model was adequate only with the destructive sampling procedure. Models for the remaining nutrients were not suitable. Although destructive sampling and desktop scanning procedure generally produced models with higher correlations they required work and time for sample preparation that might reduce the value of this NIR approach. The results suggest that both destructive and nondestructive sampling NIR calibrations can be useful to monitor macro nutrient status of teak plants grown in a nursery environment.

A non-destructive NIR spectroscopic method combined with chemometry for simultaneous assay of paracetamol and caffeine in tablets

The use of near infrared (NIR) spectroscopy to predict the concentration of two active pharmaceutical ingredients (APIs), paracetamol and caffeine, in intact tablets, has been evaluated in this study. A partial least squares (PLS) regression model was developed using spectral data obtained on a calibration set consisting of 28 formulations containing 80, 90, 100, 110 and 120% of each API. Regression models were developed for each API, both using un-processed spectral data as well as after applying various spectra pre-processing methods. Cross-validation was used to select best calibration model. The selected model was validated in terms of precision, trueness, accuracy and linearity in a concentration ranging from 90 to 110% of the targeted APIs concentration. The applicability of the method was tested on tablets containing 300 mg paracetamol and 30 mg caffeine as targeted composition, and the API content predicted by the proposed NIR-chemometric method was not statistically different from the one obtained by HPLC method, used as a reference method. Thus, the method presented in the current paper is a step forward towards the implementation NIR as useful tool for monitoring the manufacturing process of fixed-dose combination tablets with paracetamol and caffeine.

Distribution and quantification of activated platelets in platelet-rich fibrin matrices

Platelet-rich fibrin (PRF) has been widely applied in regenerative therapy owing to its simple preparation protocol. To date, the original protocol for preparing leukocyte-rich (L)-PRF has been modified to produce derivatives such as advanced (A)-PRF, concentrated growth factors (CGF), and horizontal (H)-PRF. However, these derivatives have not been rigorously compared to explore possible differences. We previously developed and validated a nondestructive near-infrared (NIR) imaging method to quantitatively examine the platelet distribution in PRF matrices. To further evaluate the characteristics of platelets in PRF, we herein examined the distribution of activated platelets. Four types of PRF matrices were prepared under different centrifugal conditions from blood samples obtained from the same healthy donors. After fixation and compression, the matrices were stained immunohistochemically without sectioning and visualized using an NIR imager. Qualitative morphological analysis revealed that whole platelets were distributed widely and homogeneously in H-PRF and A-PRF, but localized along the distal tube surface in L-PRF and CGF. Activated platelets were distributed as were whole platelets in A-PRF, L-PRF, and CGF, but localized mainly in the “buffy coat” region in H-PRF. Quantitative analysis revealed that there was no significant difference in the ratio of activated to whole platelets between PRF derivatives. These findings suggest that platelet activation is similarly induced in fibrin matrices regardless of centrifugal speed or rotor angulation. However, only the H-PRF group was distinguishable from the other PRF derivatives in terms of activated platelet distribution.

Non-destructive assessment of flesh firmness and dietary antioxidants of greenhouse-grown tomato (Solanum lycopersicum L.) at different fruit maturity stages.

Non-destructive methods have been widely recognized for evaluating fruit quality traits of many horticultural crops and food processing industry. Destructive (analytical) test, and non-destructive evaluation of the quality traits were investigated and compared for ‘Red Rose’ tomato (Solanum lycopersicum L.) fruit grown under protected environment. Fresh tomato fruit at five distinctive maturity stages namely; breaker (BK), turning (TG), pink (PK), light-red (LR), and red (RD) were labeled and scanned using the handheld near infra-red (NIR) enhanced spectrometer at a wavelength range of 285–1200 nm. The labeled tomato samples were then measured analytically for flesh firmness, lycopene, β-carotene, total phenolic content (TPC) and total flavonoids content (TFC). The results revealed that quality traits could be estimated using NIR spectroscopy with a relatively high coefficient of determination (R2): 0.834 for total phenolic content, 0.864 for lycopene, 0.790 for total flavonoid content, 0.708 for β-carotene; and 0.679 for flesh firmness. The accumulation of Lyco and β-Car rapidly increased in tomatoes harvested between the TG and the LR maturity stages. Harvesting tomatoes at BK maturity stage resulted in significantly higher flesh firmness than harvesting at the later maturity stages. Tomato fruits had the lowest TPC and TFC contents at the earliest maturity stage (BK), while they had intermediate TPC and TFC levels at LR and RD maturity stages. NIR spectroscopic measurements of fruit firmness and lipophilic antioxidants in tomato fruit at various maturity stages were partially in accordance with those estimated by destructive (analytical) methods. Based on these findings, we recommend using non-destructive NIR spectroscopy as an effective tool for predicting tomato fruit quality during harvest stage and postharvest processing.

Comparison and Application of Non-Destructive NIR Evaluations of Seed Protein and Oil Content in Soybean Breeding

A plant breeding program needs to evaluate a large number of materials for different traits within a limited time. Near-infrared (NIR) spectroscopy has been used to quickly determine seed composition in various crop species. In this study, we compared whole-seed evaluations of protein and oil content by NIR methods in soybean [Glycine max (L.) Merr.], and then discussed the application to plant breeding. The differences among the entries tested were highly significant in both traits for each method used. No significant difference but high correlation and consistency existed between DA 7250 and wet-chemistry methods. Compared with DA 7250, ZX-50 exhibited, to some extent, differences or errors. The differences of ZX-50 methods were found to be correlated with seed sizes and could be corrected using regression equations formulated for bias calculation. After correction, the differences in the predictions between DA 7250 and ZX-50 methods were insignificant. Similar to DA 7250, ZX-50 methods exhibited a high repeatability (> 98%) of the predictions. By validation with 760 bulk samples of different seed types and 240 single-plant samples, it further demonstrated that as a non-destructive, fast and cost-efficient method, ZX-50 NIR analysis with an appropriate bias correction could be used in soybean breeding, specifically suitable for single plant selection based on whole seeds.

A sampling system for flowing powders based on the theory of sampling.

An innovative chute and stream sampler system for flowing powders has been developed and tested. The system is designed for representative sampling based on the principles of the Theory of Sampling (TOS). The sampling system was used in combination with near infrared (NIR) spectroscopy to determine the drug concentration of flowing powders. The system is comprised of three parts: a chute, a stream sampler and a sample collection port. The NIR spectra were obtained at the chute, before entering the sampler, and as the powder flowed through the stream sampler. Samples were also collected from the sample collection port to be analyzed using an ultraviolet-visible (UV-Vis) reference method to determine drug content. A total of eight pharmaceutical powder blends, ranging in concentration from 10.5(%w/w) to 19.5(%w/w) of caffeine, were used to test the sampling system. Materials were characterized before blends were made to provide information on flow properties. The throughput of the system was between 30 and 35kg/h based on the flow properties of the blend. Drug concentration was effectively determined at the chute and stream sampler. The NIR calibration models showed low root mean squared errors of prediction, 0.65(%w/w) and 0.51(%w/w), for the chute and stream sampler respectively. The NIR calibration models also showed low bias values -0.36(%w/w) at the chute and 0.057(%w/w) at the stream sampler. Significant agreement was obtained between the results from the nondestructive NIR versus the destructive UV-Vis method. Variographic analysis was performed to estimate the analytical and sampling errors when determining the drug concentration at the chute and stream sampler respectively. The variographic analysis showed low analytical errors, 0.103(%w/w)2 and 0.181(%w/w)2 at the chute and stream sampler respectively. The analysis also showed that the minimum practical error (MPE) was around 0.2(%w/w)2 at both chute and stream sampler.

Near infrared spectroscopy to predict internal quality of mangoes

Consumers have high demands regarding the quality of mangoes. Fruits with internal defects lead to disappointment after sale and a decrease in purchases. The aim of this study is to predict internal quality of mangoes using non-destructive near infrared (NIR) measurements. The internal browning was measured from 3000 mangoes grown and harvested in Brazil, transported to the Netherlands and ripened. The results show a good correlation between the occurrence of internal defects and the loss of fruit firmness and increase in fruit flesh colour. NIR measurements allow sorting of mangoes for internal defects in a non-destructive manner, to ensure only mangoes without internal defects reach the consumers. This not only prevents consumer disappointment but also contributes to less waste and a more sustainable mango chain.

Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit

Quality evaluation of passion fruit is an important practice before consuming or processing. The fruit’s total soluble solids (TSS), titratable acidity (TA), and pulp content (PC) were predicted by near-infrared (NIR) spectroscopy. Prediction models were constructed by chemometrics of the partial least squares (PLS) regression on the NIR spectra from interactance spectroscopy. Accurate prediction results were obtained and showed high correlations (r) between the predicted and reference values (0.84, 0.91, and 0.99 for TSS, TA and PC, respectively). Small standard errors of prediction (SEPs) and bias were also found. A robust prediction model of pulp content provided the greatest value of the residual predictive deviation (RPD = 6.4). Variable selection effectively highlighted the important wavelengths and helped to prune the unimportant variables for the TSS, TA and PC produced calibrations with satisfactory results in the predictions (r = 0.84 – 0.98). In conclusion, nondestructive NIR spectroscopy can be a potential predictor for determining purple passion fruit quality.

Assessment of seed germinability of mechanically-damaged soybeans using near-infrared hyperspectral imaging

Mechanical damage of seeds during harvesting and postharvest handling affects their germinability, which directly impacts crop yield. The feasibility of near-infrared (NIR) hyperspectral imaging technique was studied to predict the germinability of soybeans in a rapid and non-destructive way. Soybean seeds were artificially damaged using an impact test device, and the effect of three levels of impact energy (0.12, 0.22 and 0.32 J) was studied on the germination rates of these seeds at four moisture contents (11, 13, 15 and 17%, wet basis). A multivariate statistical model (partial least squares discriminant analysis (PLSDA)) was built using the NIR spectra of the soybean samples to classify them based on their germinability. The prediction performance of the PLSDA model was defined based on its accuracy (0.94), sensitivity (0.89) and specificity (0.91). The model showed good performance in terms of accuracy and sensitivity for pre-defined classes of soybean samples, establishing that the non-destructive NIR hyperspectral imaging technique could be used to predict soybean seed germination.

Prediction of dissolution profiles by non-destructive NIR spectroscopy in bilayer tablets.

This study describes how near infrared (NIR) spectroscopy can be used to predict the dissolution of bilayer tablets as a non-destructive approach. Tablets in this study consist of two active pharmaceutical ingredients (APIs) physically separated in layers and manufactured under three levels of hardness. NIR spectra were individually acquired for both layers in diffuse reflectance mode. Reference dissolution profile values were obtained using dissolution apparatus & HPLC. A multivariate partial least squares (PLS) calibration model was developed for each API relating its dissolution profile to spectral data. This calibration model was used to predict dissolution profiles of an independent test set and results of the prediction were compared using model free approaches i.e. dissimilarity (f1) & similarity (f2) factors to assure similarity in dissolution performance.

Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Sugar beet seed oil reserves play an important role in successful germination and seedling development. The purpose of this study was to establish a non-destructive near-infrared (NIR) methodology with good predictive accuracy to quantify stored seed oil in sugar beet seed. Reflectance NIR spectra were acquired from viable monogerm seeds. Calibration equations were developed using partial least squares. The optimized calibration model reached a Pearson correlation of 0.946; an independent prediction test reached a correlation of 0.919 and a Root Mean Square Error of Prediction of 0.388. The possible role of the outer pericarp in the prediction of oil content was additionally considered. The results indicate that the model is suitable for a rapid and accurate determination of the oil content in both polished and unpolished sugar beet seeds. This NIR application might help to understand the role of seed energy reservoirs in sugar beet germination and further plant growth.