Handheld Near-infrared Spectroscopy Research Articles

Prediction of yerba mate caffeine content using near infrared spectroscopy

There is a commercial and beneficial interest of producing yerba mate leaves into different grades of caffeine. This work uses a handheld and bench near-infrared (NIR) spectroscopy to compare and predict, using partial least squares (PLS) regression, the amount of caffeine in yerba mate leaves. Standards of pure caffeine were compared, using high-performance liquid chromatography (HPLC), with extracts of yerba mate. The bench spectroscopy gave a strong confidence model of caffeine prediction, whereas the handheld related to a fair model. For first detection and initial separation of yerba mate in the field, the modeling proposed can be used to predict caffeine intensity.

Rapid and Automatic Classification of Tobacco Leaves Using a Hand-Held DLP‑Based NIR Spectroscopy Device

A hand-held near infrared (NIR) spectroscopy device is much more convenient than a traditional desktop NIR instrument. Thus, it is more suitable for the practical application. An automatic and rapid tool for grading tobacco leaves on the spot using a hand-held digital light processing (DLP)-based NIR spectroscopy device is proposed in this paper. Firstly, the spectral data of the samples is scanned with a hand-held NIR device directly from the tobacco leaves without any samples preparation procedures. Then, the training model of different classes is built and the class of each test sample is predicted by using sparse representation classification (SRC) algorithm. Comparing with the traditional linear discriminant analysis (LDA) and support vector machine (SVM) algorithms, the classification accuracy of SRC method is the highest and has the least computation time. The results show that hand-held NIR spectroscopy technology could be a novel classification tool for grading tobacco leaves in the purchasing on the spot.

Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES

Near-infrared spectroscopy (NIRS) is a potential, field-portable wood identification tool. NIRS has been studied as tool to identify some woods but has not been tested for Dalbergia. This study explored the efficacy of hand-held NIRS technology to discriminate, using multivariate analysis, the spectra of some high-value Dalbergia wood species: D. decipularis, D. sissoo, D. stevensonii, D. latifolia, D. retusa, all of which are listed in CITES Appendix II, and D. nigra, which is listed in CITES Appendix I. Identification models developed using partial least squares discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA) were compared regarding their ability to answer two sets of identification questions. The first is the identification of each Dalbergia species among the group of the six above, and the second is the separation of D. nigra from a single group comprising the other species, grouping all Dalbergia as one class. For this latter study, spectra of D. cearensis and D. tucurensis were added to the broader Dalbergia class. These spectra were not included in the first set because the number of specimens was not enough to create an exclusive class for them. PLS-DA presented efficiency rates of over 90% in both situations, while SIMCA presented 52% efficiency at species-level separation and 85% efficiency separating D. nigra from other Dalbergia. It was shown that PLS-DA approaches are far better suited than SIMCA for generating a field-deployable NIRS model for discriminating these Dalbergia.

Using portable near-infrared spectroscopy to predict pig subcutaneous fat composition and iodine value

This study tested the potential of portable near-infrared spectroscopy (NIRS) to predict fatty acids (FA) composition and iodine value (IV) of pig subcutaneous fat. Following carcass splitting, the inner layer of subcutaneous fat at the shoulder from the left carcass side was scanned using a hand-held NIRS probe (350–2500 nm) and analysed for FA composition using gas chromatography. The NIRS successfully predicted the total polysaturated fatty acids (PUFA) and n-3 FA proportions, polyunsaturated/saturated fatty acids (PUFA/SFA) ratio, and IV (R2 = 0.90–0.95; root-mean-square error of prediction, RMSEP = 0.019%–1.03% total FA). This portable technology also met the requirements for a quick screening of the proportions of total SFA, monounsaturated fatty acids (MUFA) and n-6 FA, n-6/n-3 ratio, and some individual FA such as C18:2n-6 and C18:3n-3 (R2 = 0.80–0.89; RMSEP = 0.37%–1.11% total FA). However, unreliable predictions were found for other individual FA with low variability (coefficient of variation = 4%–8%) such as C16:0, C18:0, and C18:1 (R2 = 0.60–0.77; RMSEP = 0.76%–1.00% total FA). These results show portable NIRS as a suitable technology to predict pig fat quality. In addition, this study has been successful in implementing this portable NIRS technology in a research abattoir to collect spectra directly on the carcass, which would enable carcass sorting based on fat composition or hardness for marketing purposes.

Non-destructive prediction of soluble solids and dry matter content using NIR spectroscopy and its relationship with sensory quality in sweet cherries

Near infrared spectroscopy (NIR) models developed in the spectral region between 729 and 975nm for a handheld instrument have been demonstrated to be accurate, robust and versatile for the rapid and non-destructive evaluation of soluble solids (SSC) and dry matter (DMC) content in sweet cherries at two different temperatures, 0 and 23°C. Coefficient of determination (R2) values for model calibrations ranged from 0.922 to 0.946 for SSC and from 0.910 to 0.933 for DMC, with standard errors of calibration from 0.612 to 0.792 and from 0.687 to 0.911% for SSC and DMC, respectively, depending on the variety and the temperature of the fruit. External validation of the models demonstrated they could be confidently used for ‘Chelan’ and ‘Bing’ sweet cherries; R2 values for SSC ranged from 0.726 to 0.891 and for DMC from 0.670 to 0.725 between the predicted and the observed values in the external validation set, respectively. Consumer preference evaluation for ‘Bing’ and ‘Chelan’ sweet cherries with differing SSC (16.38–28.45) and DMC (17.67–31.62%) revealed higher acceptance by consumers for cherries with high DMC as well as for cherries with high SSC. DMC was equal to or superior to SSC for predicting flavor intensity and balance of sweet to sour taste as assessed by consumers. Considering the revealed importance in sensory evaluation of DMC, as well as SSC, in the eating quality of sweet cherry, the use of handheld NIR devices, which are rapid, lightweight and user-friendly should be considered for routine, non-destructive analysis of sweet cherry quality.

Artemisia annua L.: Polyploidy and NIRS, two tools to improve breeding efficiency

Breeding a new cultivar needs 5 to 15 years according to the species and the breeding objectives from bioprospection up to cultivar registration. This is a very long time for companies developing and trading plant based products. To react more quickly to the requirements of the stakeholders, methods to accelerate the breeding procedures have to be taken into account. Among different possibilities, polyploidy induction and rapid methods to measure target traits with near infrared spectroscopy (NIRS) were tested on Artemisia annua L. Tetraploid progenies were compared to the cultivar Apollon. These tests showed no significant differences between the tetraploid plants and the cultivar Apollon for the artemisinin content, as well for the leaf and the artemisin yield. The determination of artemisinin in powder of Artemisia annua using a hand-held NIRS device showed accurate results in predicting artemininin contents. Root mean square error values of cross-validation and prediction of 0.1 % were calculated, in both cases.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

Application of Handheld NIRS to proximate analysis of animal feed and detection of melamine adulteration of soya.

Qualitative and Quantitative Models Based on Handheld NIR Spectroscopy to Monitor the Tomato Fruit Development During Early and Full Season

The present study aimed at to follow the tomato fruit development and quality by hand-held near-infrared spectroscopy. Tomato quality were followed from few days after fruit setting until harvest at commercial maturity during two seasons (spring and summer). Results showed that in both seasons, fruit can be classified from fruit setting to harvest at maturity by using qualitative models (factorial discriminant analyses). Quantitatives models based on PLS regressions allowed the prediction of soluble solids content (R=0.9, RMSE=0.1%Brix), titrable acidity (R=0.9, RMSE=0.6meq.100g-1) and color (a*, R=0.9, RMSE=5) of fruit. The accuracy of the predictions depend on the season and also on the maturity stage. the results are promising in the context of developing a tool to assist in fruit phenotyping on site. Other experiment are now necessary to improve the accuracy and the robustness of the models with including additional varieties growing under variable climatic conditions in our greenhouses.

Development of a Hand-Held near Infrared System Based on an Android OS and MicroNIR, and its Application in Measuring Soluble Solids Content in Fuji Apples

Near infrared (NIR) spectroscopy has been widely applied in the areas of agriculture, food, pharmaceutical and chemical industries because of its advantages such as its speed, non-destructive nature and simultaneous detection of multiple components. However, most existing NIR instruments are too large to be carried into the field. Herein, we have developed an interface by which the application (app) runs on a smart phone (or tablet) to drive a miniature NIR spectrometer (MicroNIR, a product of the JDSU Uniphase Corporation). This app has three modules; the first drives the hardware to control the spectrometer; the second facilitates graphical display of spectral data, spectral pretreatment according to method provided by the model file and production of a calculated result while the third is the management of models, samples, spectral parameter setting and an address book. We have used this app with the MicroNIR to measure soluble solids content (SSC) in Fuji apples in the field; results were compatible with the reference results based on an Abbé refractometer analysis. To conclude, this new hand-held NIR spectroscopy system based on android device is convenient and practical in rapid measurement of SSC of Fuji apples in the field. It has potential to be used for in situ measurement of other constituents of agriculture products.

Authenticity Control of Roasted Coffee Brands Using Near-Infrared Spectroscopy

In this work, a methodology based on near-infrared spectroscopy (NIRS) was exploited in order to discriminate between commercial coffee brands. The main advantages of this approach compared to other strategies (e.g., wet chemistry methods) are its lower cost, less labor, and lower time per analysis. Two commercial brands were discriminated among several others present in the Portuguese market. The chemometric method used to estimate discriminant models was partial least squares discriminant analysis (PLSDA). Results show that it is possible to discriminate coffee brands using this strategy with a correct classification of 100 %. The spectral region, more favorable to discrimination of roasted coffee brands, can be related with differences in the concentrations of compounds, such as, chlorogenic acid and sucrose, and also due to differences on lipid fraction. This methodology is adequate for field implementation, namely, adopting handheld NIRS instruments.

Handheld NIRS analysis for routine meat quality control: Database transfer from at-line instruments

Innovative advances in Near Infrared Spectroscopy (NIRS) instrumentation have enabled the development of new miniaturized spectrometers that combine NIRS technology with micro-electro-mechanical platforms, thus opening up new horizons for industrial NIRS applications. Many agro-food industries, laboratories and research centres already have large databases/libraries, built up over many years using NIR spectrometers; it is clearly important to preserve these data sets in order to avoid having to research and develop NIRS applications from scratch every time a new instrument appears on the market. Three standardization algorithms—Direct Standardization (DS), Piecewise Direct Standardization (PDS) and Spectral Difference by Wavelengths (SDW)—and varying numbers of standardization samples were evaluated for transferring meat quality databases from a high-performance at-line NIRS monochromator to a handheld based on micro-electro-mechanical systems (MEMS) NIRS spectrometer. The SDW algorithm and the use of 8 standardization samples yielded the best Standard Error of Prediction (SEP) values for the three chemical parameters transferred (0.72% for fat, 0.73% for moisture and 0.66% for protein). The successful transfer of the database to the MEMS-NIRS device enables a new approach for fast, low-cost, on-line/in-situ analysis of meat products.