Journal of Near Infrared Spectroscopy

Antibiotic dregs contain antibiotic residues and pose safety risks if they are used illegally in animal feed protein materials. The objective of this study was to develop a shortwave infrared (SWIR) hyperspectral imaging system (HSI) to quantify and qualify antibiotic dregs for the adulteration of feed protein materials (FPMs). Three FPMs were adulterated with oxytetracycline dregs (OD) over a range of 2%–98% (w/w). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and one-class partial least squares classifier (OCPLS) models were developed based on different spectral preprocessing techniques to predict adulterants. Furthermore, mean spectra with partial least squares (PLS) regression and images were used to predict adulterant concentrations. The results showed that FPMs and adulterated FPMs could not be completely distinguished using PCA. PLS-DA and OCPLS exhibited the highest classification accuracies (100%), and PLS-DA exhibited better recognition results for the pixel spectra. Using the mean spectra, PLS models were successfully established to predict adulteration levels in FPMs. The optimal parameters of residual predictive deviation (RPD) were 14.5, 6.1, and 7.6. Overall, the developed HSI system and optimized model demonstrated a high potential for discriminating antibiotic dregs in FPMs and allowed for quantitative evaluation.

Selective detection of toxic volatile organics in the gas-phase is required for many applications. Here we report a novel optical waveguide for near infrared (NIR) spectroscopy for quantitative detection of volatile organic compounds (VOCs), enabling sensitive and selective measurements of multiple VOCs simultaneously. The adsorption of the VOC molecules inside the nanopores allows detection at a much lower concentration compared to traditional gas-cell detection, where infrared light propagates through the gaseous molecules in the free space. Results show that a 10 mm long porous core waveguide offers a significant enhancement in signal of 1800x per millimeter pathlength compared to a 400 mm long conventional gas-cell. Moreover, lowering the temperature of the waveguide facilitates increased adsorption of molecules inside the pores and maximizes the signal as demonstrated for gaseous toluene at four different concentrations. An increase in signal by a factor of 10 is observed as the temperature decreases from 70 to 10°C. At 10°C waveguide temperature, the limit of detection for toluene is 2.5 ppm. Additionally, we measured a binary mixture of toluene and 1-octene at different temperatures, showing the selectivity of the method. The combined effect of nanopore adsorption and temperature-assisted condensation allows for selective and sensitive on-chip detection of volatile organics. The proposed waveguide sensor technology has promising potential for industrial gas leak detection, air quality monitoring, and medical applications.

Black pepper (Piper nigrum L.) is a widely used spice known for its complex chemical composition and beneficial properties, including anti-inflammatory, antioxidant, anti-cholesterol, and antimicrobial effects. The spice’s distinctive aroma and flavor are largely due to its essential oil content, which is rich in bioactive compounds such as piperine, terpenes, alkaloids, flavonoids, and volatile oils. Monoterpenes like beta-pinene, sabinene, and beta-caryophyllene are significant contributors to these properties. This research aims to predict the piperine content in black pepper using near infrared (NIR) spectroscopy. The spectral data was analyzed with various regression techniques, enhanced by machine learning to assess the accuracy of predicting piperine concentration in different brand of black pepper samples from verified suppliers. NIR absorption spectroscopy, covering the wavelength range of (900–1700) nm, was employed to assess the samples. The spectral data were processed and analyzed using principal component analysis (PCA), decision trees (DT), support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and partial least squares (PLS) regression. PCA was used to visualize the separation of the samples, while the other methods evaluated the fit of predictive models using metrics such as R² (coefficient of determination), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The performance efficacy of the regression models have been evaluated using cross validation (CV), intraclass correlation coefficient (ICC), F score, and p-value. NIR spectroscopy combined with chemometric analysis effectively predicts the piperine concentration in black pepper samples and demonstrated successfully. The predicted data using NIR spectroscopy was correlated with the reverse-phase high performance liquid chromatography (RP-HPLC) measured data as reference values of black pepper samples. This approach also holds potential for application in the quality assessment of other spices and related products.

Near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression has been widely used to predict the physical, mechanical, and chemical properties of wood. Recently, advanced modeling techniques such as deep learning and machine learning models using spectral data have increasingly been used to predict variability in wood physical and mechanical properties, but studies on chemical properties are scarce. Herein, southern pine wood samples were acquired from multiple height levels from 119 trees to yield 1007 pith-to-bark radial strips. The strips were scanned on an NIR hyperspectral imaging system and the spectral data aligned to ring-level measurements. Acetone soluble extractives content was measured on 50-mm radial length samples with 262 data points assigned to training a PLS regression model, an artificial neural network (ANN) model, and a light gradient-boosting machine learning model (LGBM). The models were then evaluated on the test dataset (140 samples). The fit statistics were good for all models, although the LGBM model (RMSE = 2.1%) performed better than the PLS regression (3.3%) or ANN (2.6%) models. The models were subsequently used to predict extractives content for 31,314 rings. The LGBM model provided biologically realistic values for all rings, whereas the PLS regression model predicted 9648 negative values, and the ANN model extrapolated well beyond the measured values (max predicted = 69.4% vs max measured = 53.2%). This study highlights real benefits of using deep learning and machine learning models compared to PLS regression models for NIR hyperspectral imaging datasets.