Portable Near-infrared Device Research Articles

Performance Comparison of Two Portable Near-infrared Devices for Rapid Authentication of Aceh Aromatic Rice ‘Sigupai’

Sigupai rice, Indonesia local aromatic rice varieties grown in South-West region of Aceh, is highly valued for its fragrance and quality, making it susceptible to adulteration. This study compares the performance of two portable Near-infrared (NIR) devices, SCiO and NeoSpectra, for rapid authentication of Sigupai rice. We evaluated 86 samples for qualitative analysis (i.e. authentic vs adulterated rice) and 44 samples for quantitative analysis (i.e. the level of adulteration). For the qualitative analysis using partial least squares-discriminant analysis (PLS-DA), the best estimation model could differentiate authentic and adulterated samples with an accuracy, sensitivity, specificity, and false positive rates of 89.29%, 92.86%, 85.71% and 14.29% for the NeoSpectra and 97.44%, 100%, 94.87%, and 5.13% for the SCiO, respectively at the validation stage. For quantitative analysis using partial least squares-regression (PLS-R), the best estimation model could estimate the level of adulteration with a coefficient of determination (R²), RMSEP, RPD, and consistency values of 0.92, 1.50%, 5.93 and 100.69% for the NeoSpectra and 0.96, 1.31%, 6.83 and 104.78% for the SCiO. Both portable NIR devices could be used as a rapid analysis tool for the authenticity of Sigupai rice with high accuracy. However, in this study the SCiO device showed a better performance. Keywords: Portable NIR device, Authentication, Aromatic rice, Rapid analysis, Sigupai variety.

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device

In the past years, near infrared (NIR) spectroscopy has been applied to the agricultural industry as a non-destructive tool to predict quality parameters, e.g., ripeness of fruit, dry matter content, and acidity. In two years, 2019 and 2020, berries of four table grape cultivars (Cotton Candy™, Summer Royal, Allison™, and Autumncrisp®) were collected during the season to obtain spectral measurements and quality data for developing predictive models based on NIR spectroscopy to be practically used in the vineyard. A SCiO™ sensor was used in 2019 for predicting the ripening parameters of Cotton Candy™; in particular, total soluble solids (TSS) (R2 = 0.95; RMSE = 0.60, RPD = 13.13), titratable acidity (R2 = 0.97; RMSE = 0.40, RPD = 7.31), and pH (R2 = 0.96; RMSE = 0.07, RPD = 26.06). With these promising results, in the year 2020, the above-mentioned table grape cultivars were all tested for TSS prediction with successful outcomes: Cotton Candy™ (R2 = 0.97; RMSE = 0.68, RPD = 7.48), Summer Royal (R2 = 0.96; RMSE = 0.83, RPD = 7.13), Allison™ (R2 = 0.97; RMSE = 0.72, RPD = 8.70) and Autumncrisp® (R2 = 0.96; RMSE = 0.60, RPD = 9.73). In conclusion, a rapid and economic sensor such as the SCiO™ device can enable a practical application in the vineyard to assess ripening (quality) parameters of table grapes. Thus, this device or similar ones can be also used for a fast sorting and screening of quality throughout the supply chain, from vineyard to cold storage.

Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy

The feasibility of using a portable near-infrared (NIR) spectrometer combined with partial least squares for discrimination analysis (PLS-DA) to identify five similar Cinnamomum wood species was investigated. To improve model reliability and identification accuracy, the effects of three main spectra preprocessing methods and their combinations were examined. Then, the model performance created by spectra collected before and after specimen surface sanding were compared. In the PLS-DA model based on spectra preprocessed by standard normal variate (SNV) and first derivative combined, the identification accuracy of the five species was above 95%, and the compared results demonstrated the surface natural change influences the NIR model performance. It was shown that a portable NIR device combined with PLS-DA can be used to rapidly and accurately identify five similar Cinnamomum wood species.

Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device.

This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950–1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square–discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

Towards the in-forest Assessment of Kraft Pulp Yield: Comparing the Performance of Laboratory and Hand-Held Instruments and Their Value in Screening Breeding Trials

The non-destructive prediction of Kraft pulp yield in standing trees using near infrared (NIR) spectroscopy has been typically performed by removing 5 mm or 12 mm increment cores from candidate trees in the forest and returning the samples to the laboratory for analysis. It would be desirable to perform in-field prediction using a portable NIR device; for example, to rapidly screen individual trees in a breeding trial prior to selecting trees for further sampling. This paper compares the performance of a laboratory-based full-range Fourier transform NIR system with two portable NIR devices operating at two different spectral ranges (950–1800 nm and 1600–2400 nm). Calibrations, explaining commercially useful amounts of variance in Kraft pulp yield ( R2 = 0.85–0.94) and cellulose content ( R2 = 0.92–0.96), were developed for the laboratory NIR and both hand-held portable NIR instruments using woodmeal. This demonstrates that the wavelength range and resolution of the portable instruments are suitable for the prediction of Kraft pulp yield and cellulose content. In an initial in-forest trial, calibration of Kraft pulp yield ranged between R2 = 0.4–0.6 across two sites and two sampling periods. While there are still a number of in-forest sampling issues to be resolved around seasonal sampling, the implication is that low-cost, hand-held NIR systems are a feasible alternative to laboratory-based NIR systems for routine assessment of Kraft pulp yield.

Improvement of Near-Infrared Spectral Calibration Models for Brix Prediction in ‘Gannan’ Navel Oranges by a Portable Near-Infrared Device

A portable near-infrared (NIR) device was developed to nondestructively predict Brix value in intact ‘Gannan’ navel oranges. This research focused on developing calibration models which were less disturbed by the challenges of portable applications. The spectra of 150 samples were collected in the wavelength range of 820–950 nm. Wavelet transformed (WT) was applied to compress the raw data for improving the optimization efficiency. Classical linear partial least squares regression and nonlinear least squares support vector regression (LSSVR) were applied to building calibration models. By comparison, both prediction precision and optimization efficiency of the compressed regression models were improved. The LSSVR models outperformed the PLS models with higher accuracy and lower error. LSSVR combined with WT compression (WT–LSSVR) produced the best correlation coefficient value (r) and the root mean squared error of prediction of 0.918 and 0.321 oBrix. Based on these results, WT–LSSVR is to be a promising method to improve precision and optimization efficiency of NIR spectral calibration models for Brix prediction in ‘Gannan’ navel oranges by the portable near-infrared device.

Clinical Evaluation of a Portable Near-Infrared Device for Detection of Traumatic Intracranial Hematomas

The purpose of this multicenter observational clinical study was to evaluate the performance of a near-infrared (NIR)-based, non-invasive, portable device to screen for traumatic intracranial hematomas. Five trauma centers collected data using the portable NIR device at the time a computed tomography (CT) scan was performed to evaluate a suspected traumatic brain injury (TBI). The CT scans were read by an independent neuroradiologist who was blinded to the NIR measurements. Of 431 patients enrolled, 365 patients were included in the per-protocol population analyzed. Of the 365 patients, 96 were determined by CT scan to have intracranial hemorrhages of various sizes, depths, and anatomical locations. The NIR device demonstrated sensitivity of 88% (95% confidence interval [CI] 74.9,95.0%), and specificity of 90.7% (95% CI 86.4,93.7%), in detecting the 50 intracranial hematomas that were large enough to be clinically important (larger than 3.5 mL in volume), and that were less than 2.5 cm from the surface of the brain. For all 96 cases with intracranial hemorrhage, regardless of size and type of hemorrhage, the sensitivity was 68.7% (CI 58.3,77.6%), and specificity was 90.7% (CI 86.4,93.7%). These results confirm the results of previous studies that indicate that a NIR-based portable device can reliably screen for intracranial hematomas that are superficial and of a size likely to be of clinical importance. The NIR device cannot replace CT scanning in the diagnosis of TBI, but the device might be useful to supplement clinical information used to triage TBI patients, and in situations in which CT scanning is not readily available.