The efficacy of using a handheld near infrared spectrometer to predict metanil yellow (MY) adulteration levels (0-30% w/w) in dried turmeric powder was tested against a benchtop near infrared spectrometer using partial least squares regression models. The differences between near infrared instruments were resolution (i.e., 1 nm (handheld) vs. 0.5 nm (benchtop)) and sample container during scanning (plastic pouch (handheld) vs. quartz glass cup (benchtop)). Prediction performance of the calibration models developed was evaluated using number of model factors ([Formula: see text]), coefficients of determination of calibration and validation ([Formula: see text] and[Formula: see text], respectively), root-mean-square errors of calibration, cross-validation, and validation (RMSEC, RMSECV, and RMSEP), ratio of prediction error to standard deviation (RPD), and limits of detection (LOD) and quantification (LOQ). The best benchtop calibration models were based on spectral data preprocessed with Savitzky–Golay first derivative algorithm for the benchtop near infrared and standard normal variate for the handheld near infrared, yielding low[Formula: see text], high [Formula: see text] and[Formula: see text], low RMSEC, RMSECV, RMSEP, and high RPD. The LOD and LOQ for both spectrometers were 0.33 and 1.10%, respectively, and no significant difference was found between the predicted [Formula: see text] values by the benchtop and handheld near infrared spectrometers. The models were, in general, not sensitive to sample source and size of the validation set. When spectra from the benchtop near infrared were standardized using a reverse standardization strategy, calibrated against[Formula: see text], and transferred to the handheld near infrared, prediction performance dropped, from [Formula: see text] of 0.99 to 0.98, RMSEP increased from 0.96% to 1.53%, and [Formula: see text] decreased from 10.1 to 6.3. Despite the reduced prediction performance, the handheld near infrared with a transferred calibration model from the benchtop near infrared was still useful for screening, quality control, and process control applications.
Turmeric sourced from six retailers was processed into a powder and adulterated with metanil yellow (MY) at concentrations of 0.0 to 30% (w/w). A handheld near-infrared spectrometer was used to obtain spectral scans of the samples, which were preprocessed using Savitzky-Golay first-derivative (SG1) approximation using 61 smoothing points and second-order polynomial. The preprocessed spectra were analyzed using principal component analysis (PCA) followed by classification by soft independent modeling class analogy (SIMCA) and were used to group the adulterated turmeric powder samples according to the source (i.e., processor) of adulteration. Results showed the first principal component (PC1) of PCA models was sensitive to adulteration level, but when coupled with SIMCA, unadulterated and adulterated samples could be classified according to their source despite having high levels of MY. At 5% level of significance, all of the samples were correctly classed for origin during validation. Some samples were classified under two groups, indicating possible inherent similarities. When the PCA model was built using only unadulterated samples, the PCA-SIMCA model could not classify the adulterated samples but could classify those with very low levels (≤2%, w/w) of MY, allowing for segregation of adulterated samples but not identification of sources. The combination of near-infrared and PCA-SIMCA modeling is a great tool not only to detect adulterated turmeric powder but also, potentially, to deter it in the future because the source of adulterated food can be traced back to the source of adulteration. HIGHLIGHTS
HighlightsA transportable spectrometer performed similarly to benchtop spectrometers to predict forage quality.Absorption bands in the second and third overtone regions were used to predict forage quality with ample accuracy.A low-cost handheld spectrometer was useful for routine screening of forage for nitrogen content. Abstract. Assessing the nutritional composition of animal feed and forage materials is important to achieve high animal productivity and wellness. Precision nutrition programs that use NIR technology can determine the nutritional composition of feed and forage quickly and simply, generating actionable information such as total nitrogen (N), acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) contents, as well as in vitro dry matter digestibility (IVDMD). Recent advances in optics and microelectronics have allowed for the development of handheld spectrometers that are portable, robust, and user-friendly. However, are the handheld units accurate enough to predict nutritional content of animal feed? In this study, the performance of two handheld NIR spectrometers to predict the nutritional content of forage based on N, ADF, NDF, ADL, and IVDMD was evaluated by comparing them to two benchtop NIR spectrometers often used in feed and forage analysis. The forage samples comprised switchgrass (Panicum virgatum L), big bluestem (Andropogon gerardi), and Indiangrass (Sorghastrum nutans). The first handheld spectrometer covers 780-2500 nm with a spectral interval (??) of 1 nm, while the second handheld spectrometer is a palm-sized smartphone spectrometer covering 900-1700 nm with ?? = 4 nm. The benchtop spectrometers both cover 400-2500 nm with ?? = 2 nm. Forage samples were scanned on each spectrometer and divided into calibration (n = 143) and validation (n = 35) sets. Partial least squares (PLS) regression was used to calibrate all spectrometers using mean-centered spectral data that had been preprocessed using Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm with 9-63 smoothing points. Results showed that PLS models that best predicted N using the benchtop spectrometers had lower standard error of prediction (SEP = 1.24-1.28 g kg-1) and higher ratio of prediction to deviation (RPD = 3.66-3.78) compared to the models developed based on spectra collected from the handheld spectrometers (SEP = 1.46-1.78 g kg-1; RPD = 2.39-2.84). ADF, NDF, and ADL were variable and generally poorly predicted using spectra from the benchtop spectrometers (SEP = 10.02-33.19 g kg-1;RPD = 1.71-2.24), and even more so using the handheld spectrometers (SEP = 10.63-32.57 g kg-1;RPD = 1.64-2.47). Predicting IVDMD was similar for both sets of benchtop (SEP = 40.00-41.73 g kg-1; RPD = 2.24-2.34) and handheld (SEP = 34.46-40.84 g kg-1; RPD = 2.29-2.72) spectrometers. These results show that the handheld devices can be used for screening of forage samples based on N, which is a closely monitored component in animal feed and forage, as well as IVDMD, an important forage quality index. Keywords: Forage, Portable spectrometer, Spectroscopy, Screenin, Ruminant nutrition.
<abstract> <p>Two handheld near infrared (NIR) spectrometers were used to quantify crude protein ($CP$) content of mixed forage and feedstuff composed of Sweet Bran, distiller's grains, corn silage, and corn stalk. First was a transportable spectrometer, which measured in the visible and NIR ranges (320–2500 nm) with a spectral interval of 1 nm (H1). Second was a smartphone spectrometer, which measured from 900–1700 nm with a spectral interval of 4 nm (H2). Spectral data of 147 forage and feed samples were collected by both handheld instruments and split into calibration ($n$ = 120) and validation ($n$ = 27) sets. For H1, only absorbances in the NIR region (780–2500 nm) were used in the multivariate analyses, while for H2, absorbances in the second and third overtone regions (940–1660 nm) were used. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using mean-centered data that had been preprocessed using standard normal variate (SNV) or Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm. PCA models showed two major groups—one with Sweet Bran and distillers grains, and the other with corn silage and corn stalk. Using H1 spectra, the PLS regression model that best predicted $CP$ followed SG1 preprocessing. This model had low root mean square error of prediction ($RMSEP$ = 2.22%) and high ratio of prediction to deviation ($RPD$ = 5.24). With H2 spectra, the model best predicting $CP$ was based on SG2 preprocessing, returning $RMSEP$ = 2.05% and $RPD$ = 5.74. These values were not practically different than those of H1, indicating similar performance of the two devices despite having absorbance measurements only in the second and third overtone regions with H2. The result of this study showed that both handheld NIR instruments can accurately measure forage and feed $CP$ during screening, quality, and process control applications.</p> </abstract>
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