Low Standard Error Of Prediction Research Articles

Model Exploration and Application of Near-Infrared Spectroscopy for Species Separation and Quantification during Mixed Litter Decomposition in Subtropical Forests of China

Litter of different species coexists in the natural ecosystem and may induce non-additive effects during decomposition. Identifying and quantifying the origins of species in litter mixtures is essential for evaluating the responses of each component species when mixed with co-occurring species and then unraveling the underlying mechanism of the mixing effects of litter decomposition. Here, we used near-infrared spectroscopy (NIRS) to predict the species composition and proportions of four-tree species foliage mixtures in association with litter crude ash and litter decomposition time. To simulate the whole mixed litter decomposition process in situ, a controlled mixture of four tree species litter leaves consisting of 15 tree species combinations and 193 artificial mixed-species samples were created for model development and verification using undecomposed pure tree species and decomposed litter of single tree species over one year. Two series of NIRS models were developed with the original mass and ash-free weight as reference values. The results showed that these NIRS models could provide an accurate prediction for the percentage of the component species from in the litter leaf mixture’s composition. The predictive ability of the near-infrared spectroscopy model declined marginally with the prolonged litter decomposition time. Furthermore, the model with ash-free litter mass as a reference exhibited a higher coefficient of determination (R2) and a lower standard error of prediction (RMSECV). Thus, our results demonstrate that NIRS presents great potential for not only predicting the organic composition and proportion in multi-species mixed samples in static conditions, but also for samples in dynamic conditions (i.e., during the litter decomposition process), which could facilitate evaluation of the species-specific responses and impacts on the interspecific interactions of co-occurring species in high-biodiversity communities.

What’s in Your Fruit Juice?—Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy

Fruit juices (FJ) have gained widespread global consumption, driven by their perceived health benefits. The accuracy of nutrition information is essential for consumers assessing FJ quality, especially with increasing concerns about added sugars and obesity risk. Conversely, ascorbic acid (Vitamin C), found in nature in many fruits and vegetables, is often lost due to its susceptibility to light, air, and heat, and it undergoes fortification during FJ production. Current analytical methods for determining FJ components are time-consuming and labor-intensive, prompting the need for rapid analytical tools. This study employed a field-deployable portable FT-IR device, requiring no sample preparation, to simultaneously predict multiple quality traits in 68 FJ samples from US markets. Using partial least square regression (PLSR) models, a strong correlation (RCV ≥ 0.93) between FT-IR predictions and reference values was obtained, with a low standard error of prediction. Remarkably, 21% and 37% of FJs deviated from nutrition label values for sugars and ascorbic acid, respectively. Portable FT-IR devices offer non-destructive, simultaneous, simple, and high-throughput approaches for chemical profiling and real-time prediction of sugars and acid levels in FJs. Their handiness and ruggedness can provide food processors with a valuable “out-of-the-laboratory” analytical tool.

Sorption Kinetics and Equilibrium Isotherms of Phosphine Gas into Wheat Kernels

Highlights The pseudo-first order kinetic model fits well with the experimental data of sorbed phosphine versus time. Sorption isotherm curves were developed using Langmuir, Freundlich, and Redlich-Peterson models. Phosphine adsorption capacity of wheat kernels increased with increase in applied concentration (400-2400 ppm). Abstract. Phosphine (PH3) is the most used fumigant in the U.S. due to its low price, ease of use, and wide accessibility. With the growing concerns of phosphine-resistant insect pests, the sustainability of PH3 as an effective fumigant has been put at risk. Sorption equilibrium data is critical for improving the accuracy of modeling studies for phosphine-wheat fumigation systems and would clarify the PH3 uptake and sorption capacity of wheat. The objectives of this study were to determine the effect of initial concentration (from 400 to 2400 ppm) on the equilibrium concentration for PH3 in wheat kernels and on cumulative and daily PH3 sorption through time. Kinetic data showed the sorption process was time-dependent and occurred in two phases: an initial faster adsorption phase, followed by a phase with a slower sorption rate as the grain and PH3 reached equilibrium. Pseudo-first and pseudo-second order models were fit to PH3 concentrations versus time experimental data. The pseudo-first order model provided better equilibrium estimates and was used for the sorption isotherm analysis. Langmuir, Freundlich, and Redlich-Peterson sorption isotherm models were fit to the plot of equilibrium headspace gas concentration versus sorbed PH3 quantity. All three models had low standard errors of prediction (0.46-0.47). These PH3 sorption kinetics and values of total sorbed quantity at equilibrium are valuable for modeling the rate and maximum quantity of PH3 uptake in wheat. Keywords: Fumigation, Kinetics, Phosphine, Sorption Isotherm, Wheat.

Determination of quality traits and possible adulteration of molasses using FT-IR spectroscopy: A study from Turkish market.

We aimed to develop portable Fourier transform infrared (FT-IR) spectroscopy-based prediction algorithms for the key quality characteristics (soluble solids, water activity, pH, sucrose, glucose, fructose, fructose/glucose, hydroxymethylfurfural) of various types of molasses, establish their legitimacy, and create a model to separate them based on their botanical origin. Samples labeled as carob (n=27), grape (n=24), Juniper (n=13), and mulberry (n=12) were purchased from different local markets in Turkey. Labeling issues were revealed in five carob and seven grape molasses, and those samples classified as non-authentic by the FT-IR algorithms were corroborated by reference analysis. Partial least squares regression models generated to predict the key quality traits of Turkish molasses demonstrated excellent correlation with reference analysis (R2Val≥0.96) and low standard error of prediction (SEP≤2.88). The FT-IR sensor provided a feasible approach for molasses testing to assess its quality through manufacturing and storage, also provided a powerful tool to -ensure proper product labeling.

Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry.

Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Its preparation involves several unit operations, including fermentation, which is essential in gari production. Fermentation brings about specific biochemical changes in cassava starch due to the actions of lactic acid bacteria. Consequently, it gives rise to organic acids and a significant reduction in the pH. Consumer preferences for gari are influenced by these changes and impact specific functional characteristics, which are often linked to cassava genotypes. Measurement of these functional characteristics is time-consuming and expensive. Therefore, this study aimed to develop high-throughput and less expensive prediction models for water absorption capacity, swelling power, bulk density, and dispersibility using Near-Infrared Reflectance Spectroscopy (NIRS). Gari was produced from 63 cassava genotypes using the standard method developed in the RTB foods project. The prediction model was developed by dividing the gari samples into two sets of 48 samples for calibration and 15 samples as the validation set. The gari samples were transferred into a ring cell cup and scanned on the NIRS machine within the Vis-NIR range of 400-2,498nm wavelength, though only the NIR range of 800-2,400nm was used to build the model. Calibration models were developed using partial least regression algorithms after spectra pre-processing. Also, the gari samples were analysed in the laboratory for their functional properties to generate reference data. Results showed an excellent coefficient of determination in calibrations (R2 Cal) of 0.99, 0.97, 0.97, and 0.89 for bulk density, swelling power, dispersibility, and water absorption capacity, respectively. Also, the performances of the prediction models were tested using an independent set of 15 gari samples. A good prediction coefficient (R2 pred) and low standard error of prediction (SEP) was obtained as follows: Bulk density (0.98), Swelling power (0.93), WAC (0.68), Dispersibility (0.65), and solubility index (0.62), respectively. Therefore, NIRS prediction models in this study could provide a rapid screening tool for cassava breeding programs and food scientists to determine the food quality of cassava granular products (Gari).

A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp

A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of Δ9-tetrahydrocannabinol (Δ9-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91–0.95) and a low standard error of prediction (SEP = 0.02–0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.

Non-destructive assessment of amylose content in rice using a quality inspection system at grain elevators

Amylose content is an important determinant of rice quality. Its accurate, non-destructive assessment is a challenge to the industry. We examined whether the automatic quality inspection system accurately measured the amylose content. Brown and milled rice models were calibrated with 902 samples produced between 2008 and 2017 in Hokkaido, Japan, and validated individually by samples collected in 2018 (n = 33) and 2019 (n = 71) from several grain elevators within the region. Models were developed by processing the automatic system data comprising of a near-infrared spectrometer, a visible light grain segregator, combined analysis of chemometrics, and by merging low and ordinary amylose variety validation results. The lower standard error of prediction (<0.52%) and a higher ratio of performance deviation (>4.0) in both models enabled accurate non-destructive assessment of amylose content. Hence, the automatic quality inspection system is a useful tool for meeting the demands of higher quality and palatability of rice.

In-field screening of trans-fat levels using mid- and near-infrared spectrometers for butters and margarines commercialized in the Peruvian market

We aimed at developing fast and accurate predictive algorithms to quantify trans-fat, conjugated linoleic acid (CLA), and other fatty acids using portable and handheld infrared sensors for butter and margarine products. Butter (n = 21) and margarine (n = 15) samples were collected from local grocery stores in Lima, Peru. Their fatty acid content was determined by gas chromatography fatty acid methyl ester analysis (GC-FAME). Infrared spectra were collected using portable (five-reflections) and handheld (single-reflection) infrared (FTIR-ATR) spectrometers and a palm-sized Near-Infrared (FT-NIR) sensor. None of the margarine samples, except those made with partially hydrodenated oils (PHOs), contained trans-fat, and the trans-fat levels in the butters ranged from 0.24 to 0.62 g trans-fat/serving. Partial least squares regression models showed strong correlation (RPre ≥0.91), low standard error of prediction (SEP≤2.62), and high predictive performance based on the ratio of prediction to deviation (RPD: 1.4–15.1) and Ratio of Error Range (RER: 5.7–56.9). Quantification and classification models obtained with the five-reflections FTIR-ATR system exhibited best performances in terms of RPre, SEP, RPD, and RER, followed by the single-reflection FTIR-ATR and FT-NIR systems, respectively. Portable and handheld devices therefore can provide real-time and in situ results to the fat/oil and dairy industry and regulatory agencies for actionable decisions.

Pumice-supported ZnO-photocatalyzed degradation of organic pollutant in textile effluent: optimization by response surface methodology, artificial neural network, and adaptive neural-fuzzy inference system

A heterogeneous photocatalysis was adopted to treat textile industry effluent using a combination of pumice-supported ZnO (PUM-ZnO) photocatalyst and solar irradiation. The visible light-responsive PUM-ZnO photocatalyst was prepared via the impregnation method and characterized using various spectroscopic techniques. The photocatalytic degradation process was modeled via response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS), while the optimization of the three independent parameters significant to the photocatalytic process was carried out by a genetic algorithm (GA) and RSM methods. The low standard error of prediction (SEP) of 0.56-1.75% and high coefficient of determination (R2) greater than 0.96 for the models developed indicated that they adequately predicted the photodegradation process with high accuracy in the order of ANFIS > ANN > RSM. The process optimization results from the developed models showed that GA performed better than RSM. The best optimal condition (3.29g/L catalyst dosage, 45.85min irradiation time, and 3.13 effluent pH) that resulted in maximum degradation efficiency of 99.46% was achieved by the ANFIS model coupled with GA (ANFIS-GA).

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (RPre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.

Classification of adulterated Para rubber sheet using a near infrared hyperspectral imaging system: A comparison between reflectance and transflectance modes

Near infrared hyperspectral imaging in the range 900–1700 nm was applied to classify Para rubber sheets produced from sulfuric acid coagulated latex and formic acid coagulated latex. In total, 200 Para rubber sheets were used for the development of the classification model using partial least squares discriminant analysis. Two types of sample presentation based on the reflectance and transflectance modes were compared to classify performance. The results indicated that both modes of measurement provided good classification with 98.33% accuracy. However, the transflectance mode yielded better performance than the reflectance mode as the former model had a higher correlation coefficient (0.88), lower standard error of prediction (0.176) and fewer preprocessing methods. The best transflectance mode was based on the absorbance pre-processed using the standard normal variate and the Savitzky-Golay second derivative whereas the best reflectance mode was developed using the absorbance pre-processed with a combination of Savitsky-Golay smoothing, the standard normal variate and the Savitzky-Golay second derivative. The fewer preprocessing methods in the transflectance mode suggested that less noise was present in the individual spectra compared to the spectra gathered in the reflectance mode. There was a clear difference between the color-mapped images of Para rubber sheets coagulated using sulfuric acid and formic acid, especially with the transflectance model. Each pixel in the images could be more accurately classified by the transflectance model.

Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.

Application of hand-held near-infrared and Raman spectrometers in surface treatment authentication of cork stoppers

The aim of this paper was to evaluate the potential of using near-infrared (NIR) spectroscopy and multivariate analysis as a rapid tool to non-destructively determine the presence of surface treatments applied to cork stoppers. Density and dimensions of 6 closure varieties were characterized and the extraction force was measured on those produced for still wines. Cork stoppers were also analyzed using hand-held NIR and Raman spectrometers. Soft independent modelling of class analogy (SIMCA) models showed significant differences among treated and untreated samples, linked to components of the coating agents applied (silicone and paraffin). SIMCA model’s classification performance was tested and high sensitivity (93.33 %) and specificity (100 %) values were obtained. Partial least squares regression (PLSR) model accurately predicted the extraction forces measured with low standard error of prediction (SEP = 4.0 daN). Our results are promising for the future application of this technology in cork industry, reducing time and economic losses.

Robustness of hyperspectral imaging and PLSR model predictions of intramuscular fat in lamb M. longissimus lumborum across several flocks and years

The percentage of intramuscular fat content of lamb meat is a key index of consumer acceptability. Hyperspectral imaging is a potential technique for in-line measurements of intramuscular fat in fresh meat. However, little work has been conducted to investigate the robustness of hyperspectral imaging data and associated multivariate models over time. Fifteen trials consisting of eight independent flocks across five years were used to quantify robustness of partial least squares regression (PLSR) models developed using data collected with the same imaging system. Two models were developed; one using data from the first year of the trials, and a progressive model that cumulatively includes data in chronological order. The two models performed similarly, in terms of the coefficient of determination (R2), standard error of prediction (SEP) and bias, when experimental conditions were consistent. However, under varying imaging conditions, the progressive model was able to account for this variability resulting in higher R2 and lower SEP.

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

Comparison of a Low-cost Prototype Optical Sensor with Three Commercial Systems in Predicting Water and Nutrient Contents of Turfgrass

ABSTRACT Chemical soil and plant analyses are time-consuming, expensive, and labor-intensive. There are some optical systems used for this purpose; however, they are expensive and require expertise for their operation. The aim of this study was to develop a low-cost prototype optical sensor and compare it with three commercial systems (GreenSeeker NDVI (Normalized Difference Vegetation Index) meter, chromameter, Fourier transform-near infrared reflectance spectroscopy (FT-NIRS)) to determine water and nutrient concentrations including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn) of turfgrass (Lolium perenne L.). Study was conducted on an experimental field to which four different levels of nitrogen fertilizer were applied. Prediction models were developed using PLSR (Partial Least Square Regression) and their performances were evaluated using the criteria of SEP (Standard Error of Prediction) and R2. With the prototype optical sensor, NDVI gave the best result among 10 different vegetation indices for the prediction of water (SEP = 1.43%) and N (SEP = 0.28%). The best results (lowest SEP) were obtained with the FT-NIRS. However, there are some disadvantages of this system along with the other two instruments (chromameter and NDVI meter) of being expensive and requiring expertise in their operation. Low-cost and easy-to-use prototype optical sensor gave similar results with the NDVI meter and chromameter to predict water and nutrient concentrations except K, Cu, and Zn. An optical sensor similar to the prototype sensor could be developed commercially with low cost and used to estimate the water and nutrient concentration of turfgrass.

Comparing the Use of Handheld and Benchtop NIR Spectrometers in Predicting Nutritional Value of Forage

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.

Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis.

The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (n = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08–3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (n = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products.

In Situ Monitoring of Sugar Content in Breakfast Cereals Using a Novel FT-NIR Spectrometer

This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.

Prediction of the total and standardized ileal digestible amino acid contents from the chemical composition of soybean meals of different origin in broilers

The objective of this experiment was to determine total amino acid (TAA) content, standardized ileal digestibility (SID) of crude protein, and standardized ileal amino acid digestibility in 9 sources of soybean meal (SBM) of different origin and to subsequently establish equations for predicting the TAA content and concentration of standardized ileal digestible amino acids (SIDAA) based on their protein content and other proximate components. Concentration of SIDAA of the samples was also predicted using TAA values. A total of 160 1-day-old male broiler chicks were randomly assigned to 10 dietary treatments consisted of 9 semipurified diets containing one SBM (200 g of crude protein/kg) as the only source of dietary amino acid (AA) and one N-free diet to determine endogenous ileal AA flow. The birds were fed with a standard diet from 0 to 18 D of age, and experimental diets were fed from 19 to 24 D of age. The fitness of the models of the study was tested using the adjusted coefficient of determination (R2) value, P-value regression and coefficients, and standard error of prediction (SEP). The coefficient of SID for Lys and Cys among SBM varied from 86.7 to 96.3 and 74.1 to 89.3, respectively, with significant difference (P < 0.05). In equations based on protein content, the adjusted R2 value ranged from 40.7 (Ile) to 99.6 (Met) and 37.2 (Met + Cys) to 99.6 (Met) for TAA content and concentration of SIDAA, respectively. Inclusion of other proximate components of test samples (e.g., crude fiber, neutral detergent fiber, acid detergent fiber, ash, gross energy, and so on) into the regression equation increased the adjusted R2 value and decreased the SEP. The results of linear regression revealed that it is possible to satisfactorily estimate the TAA content and concentration of SIDAA of SBM through its protein content and other proximate components, but the prediction equations based on other proximate components were more accurate in terms of reflecting the measured results; however, additional time and costs were associated with this approach. It is also possible to estimate the concentration of SIDAA through TAA values with reasonable accuracy and lower SEP.