Mean Standard Error Of Prediction Research Articles

Paddy moisture on-line detection based on ensemble preprocessing and modeling for combine harvester

The on-line detection of paddy moisture content (MC) during harvest has gained increasing interest recently due to its unique role for the control of combine harvester, yield evaluation and post-harvest grain handling operations. However, it is very difficult to achieve good performance under the complex and changeable situation during field harvest. In this study, paddy varieties, paddy flow, feeding types and algorithms were comprehensively considered to optimize the MC detection method. Firstly, an on-line near-infrared sensing system supplemented for grain tank of combine harvester was designed, and spectra were collected under the most common and essential detecting conditions, which including paddy varieties, feeding types and straw effect. Then, ensemble preprocessing, parameter optimization and accuracy test were performed. The best result of all conditions was extreme learning machine (ELM) coupled with the ensemble preprocessing of orthogonal signal correction with savitzky-golay (OSC + SG). The root mean standard error of prediction (RMSEPV) of this method after validation on unknown sample was as low as 1.0791% w.b, and the residual predictive deviation (RPDV) was higher than 3.5646. Stability tests were carried out under conditions of varying feeding types and straw quantities. The results showed that ELM had enough robustness to cope with complex detecting conditions and maintain proper accuracy as the mean value of repeatability, conditions and reproducibility were calculated as 0.0213%, 0.4471% and 0.6868% w.b, respectively. Despite the preliminary feasibility for on-line MC measurement of paddy, the on-line near-infrared sensing system needs to be verified on combine harvester during harvest.

The utility of a near-infrared spectrometer to predict the maturity of green peas (Pisum sativum)

This work evaluated the utility of an embedded diode array near-infrared reflectance spectrometer to predict the hardness (maturity) of green peas. Partial least squares, support vector machine (SVM) and artificial neural network regression models were developed to predict tenderometer reading (TR) from near-infrared spectra over the range of maturities experienced throughout a harvest season. The SVM model achieved the best performance in an independent data set collected in a second harvest season. Here, the SVM model explained 83% of the variation in TR with a root mean standard error of prediction (RMSEP) of 4.4 TR units over a range of TR from 65 to 104. The RPD of 2.3 achieved in this study exceeds the threshold for rough screening but falls short of replacing the tenderometer for quality control.

Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination

The application of near-infrared reflectance spectroscopy (NIRS) and multivariate analysis for determining the seed germination rate of corn genotypes was assessed. Seed samples about 90 gr belong to commercial and local corn varieties at various ages were scanned with FT-NIRS on the reflectance mode from 1000 to 2500 nm wavelength. Filter paper technique showed the seed germination rates varied between 18-100% depending on the genotypes after 7 days at ±25°C. Partial least squares regression (PLSR) was applied to the reference values corresponding to the spectra. The best statistical results obtained from the pre-treatment combinations of Smooth Savitzky-Golay 9 Points (sg9), MSC full and normalization to unit length (nle). The regression coefficient of calibration (R2C) and prediction (R2P) of the created NIRS calibration via chemometric software NIRCal are realized 0.97 and 0.98 respectively for the property of corn germination rate. The standard error of both calibration (SEC) and prediction (SEP) were almost overlapping (4.17%, 4.61% respectively). The prediction accuracy of the final NIRS model was quite reasonable with the acceptable root mean standard error of prediction (RMSEP) as 8.88%. According to the residual predictive deviation (RPD) index (4.18), the accuracy of the NIRS model regarded as in the best category. Therefore, the NIRS model developed here is sufficient to predict the corn seed germination rate very fast and non-destructively without using any regents.

Quality assessment of frying oil using short-chain fatty acid profile and infrared spectrum coupled with partial least squares

The deterioration of soybean oil (SO), palm oil (PO), and rapeseed oil (RO) for deep-fat frying chicken breast meat (CBM) and potato chips (PCs) was evaluated based upon their short-chain fatty acids (SCFAs) profiles as well as free fatty acid (FFA) content, carbonyl value (CV), and total polar compounds (TPC) content. In addition, Fourier-transform infrared (FTIR) spectra of oils combined with partial least squares were applied to predict the FFA content, CV, and TPC content in oils. Results showed that the formation of SCFAs was accompanied the reduction of unsaturated fatty acids during 12 h/day for 3 days. For frying PCs with SO, the heptanoic acid (C7:0) continuously increased from 0.01 to 0.15 g/kg and octanoic acid (C8:0) increased from 0.008 to 0.65 g/kg, while the contents of linoleic and linolenic acids reduced from 45.69–41.43 to 4.03–3.20%, respectively. For frying CBM with RO, the contents of C7:0, C8:0, and nonanoic acid were in ranges of 0.05–0.38, 0.03–1.19, and 0.11–0.17 g/kg, while contents of linoleic and linolenic acids reduced from 22.15–18.89 to 7.83–6.21%, respectively. The FFA content, CV, and TPC content in oils predicted by the constructed linear regression model were in ranges of 0.21–2.61 KOH g/kg, 9.76–58.98 meq/kg, and 8.02–36.91%, respectively. The root mean standard error of prediction, coefficient of determination (R2) for the validation set, and ratio of prediction to deviation for all validation analyses were in ranges of 0.2127–6.7364, 0.8717–0.9608, and 1.1548–4.2984, respectively. A logistically positive relationship (R2 = 0.7982-0.9700) between the content of C8:0 and CV or TPC content was found in frying oils used for frying both CBM and PCs. Thus, both the measurement of SCFAs and FTIR analysis could be used to efficiently monitor the deterioration of frying oils during deep-fat frying.

Terahertz spectroscopy and machine learning algorithm for non-destructive evaluation of protein conformation

Given the condition that protein conformation and activity are highly susceptible to environment factors such as temperature and pH, evaluation of protein conformation and activity is urgently needed in many fields. For example, most protein drugs need a stable and proper environment during production, storage and transportation, and it’s an enormous challenge to maintain protein activity throughout the whole process. Therefore, it’s necessary to ensure the safety and effectiveness of protein drugs by monitoring their activity before use. In our study, we presented an improved method for non-destructive evaluation of protein conformation and biological activity by terahertz spectroscopy combined with t-SNE-XGBoost. Firstly, bovine serum albumin (BSA) samples heated to different temperature were measured with THz-TDS. The obtained results indicated that native-conformation BSA will undergo transient states in the process of temperature induced denaturation. However, for any single given sample, it’s difficult to identify its conformation and activity directly by using the measured raw terahertz data. Therefore, we applied several different algorithms to the raw data for recognition of BSA samples with different conformation and activity induced by temperature. Finally, the models obtained by different algorithms were evaluated by calculating the root mean standard error of prediction (RMSEP) and the correlation coefficient of prediction ($$R_p$$). The THz-TDS plus t-SNE-XGBoost proved to be an effective non-destructive and label-free method for evaluation of protein conformation and activity. It can provide a new technique in many applications, such as pharmaceutical industry, clinical diagnosis and quality control.

Modeling for SSC and firmness detection of persimmon based on NIR hyperspectral imaging by sample partitioning and variables selection

Nondestructive detecting for persimmon’s internal quality is meaningful for post-harvest processing. This study focused on the modeling of soluble solid content (SSC) and firmness (FM) determination of persimmon with near-infrared (NIR) hyperspectral imaging within 900–1700 nm. Sample partitioning and variables selecting were used to optimize the partial least squares (PLS) regression detective models. Three sample partitioning methods included Kennard-stone (KS) algorithm, sample set partitioning joint x-y distances algorithm (SPXY) and random selection (RS) methods were adopted. Monte Carlo uninformative variables elimination (MC-UVE), competitive adaptive reweight sampling method (CARS) and successive projection algorithm (SPA) were applied for feature variables selection. For SSC and FM detecting, the best models were SPXY-MC-UVE-CARS-PLS model with 12 feature variables and Savitzky-Golay-RS-CARS-PLS with 7 feature variables respectively. The 12 and 7 feature variables are grouped together to build PLS models for SSC and FM determination and after the evaluation the regression coefficient of each variables, finally 10 and 9 selected wavelengths were selected to SSC and FM detection respectively. The final models obtained coefficient of determination (Rp2) of 0.757, root mean standard error of prediction (RMSEP) of 1.404 OBrix and Rp2 of 0.876, RMSEP of 0.395 kg/cm2 for SSC and FM detection respectively. Meanwhile, we obtained the SSC and FM distribution maps which could give help to visual detection. The results in this study could provide reference for the development of online classification equipment with multi-indicators detection for persimmon.

Rapid and simultaneous determination of physical and chemical properties of asphalt by ATR-FTIR spectroscopy combined with a novel calibration-free method

Determining wax content, softening point, and penetration of asphalt products by traditional methods is too time-consuming to meet the instant need in practice. Although infrared spectroscopy combined with multivariate calibration may realize rapid determination of asphalt, its modeling and maintenance are very labor-intensive and challenging. In this paper, a novel method combining attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with a fast algorithm of spectrum representation (SR) was proposed to rapidly and expediently determine the properties. The proposed method achieves calibration-free, effectively overcoming the drawback of the modeling methods. The feasibility of the new method was demonstrated using 431 asphalt samples produced by different kinds of crude oil. The root mean standard error of prediction (RMSEP) of wax content, softening point, and penetration by SR method are 0.14%, 0.55 °C and 4.71 (0.1 mm), respectively. Each of them is below the reproducibility of corresponding standard test method. Moreover, the new method is compared with other two commonly used methods, partial least squares regression (PLS) and local modeling by densification (LMD), in detail. The results show that the new method not only can solve the disadvantages of PLS modeling effectively, but also is remarkably superior to LMD method, in repeatability and speed, and robustness for predicting the sample in the region with lower density and unreasonable distribution of the samples in data bank.

Quantitative evaluation of vitrinite reflectance in shale using Raman spectroscopy and multivariate analysis

The current research builds upon a previously published study that demonstrated the combination of Raman spectroscopy coupled with multivariate analysis (MVA) for the prediction of thermal maturity in coal by evaluating the efficacy of this method for the prediction of thermal maturity in shale. MVA techniques eliminate analyst bias in peak-fitting methods by using the full Raman spectrum, and then extricating the important spectral regions for distinguishing samples and building accurate, robust models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.58–4.59%) for 53 geographically diverse shale chip samples, and 43 shale powder samples. Separate PLS models were built using Raman spectra from shale chips or powders. The calibration sets were validated using approximately one-third of the samples to rigorously assess the predictive accuracy of the models. The root mean standard error of prediction was 0.24 for the shale chip model, and 0.28 for the shale powder model. The coefficients of determination (R2) for the cross-validated data sets were identical (0.90, chips; 0.90, powders), revealing a strong linearity despite the geographic and age diversity of the samples. This study demonstrates the validity of using PLS models for the prediction of shale VRo from Raman spectra. The MVA method described herein presents a Raman alternative to the VRo industry benchmark for assessing thermal maturity in shale that is not imperiled by the shortcomings and subjectivity of peak-fitting methods.

Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

Dynamic Modeling of Carbon Metabolism During the Dormant Period Accurately Predicts the Changes in Frost Hardiness in Walnut Trees Juglans regia L.

The leafless period is often considered as inactive, although trees have to actively modulate their metabolism through the cold acclimation/deacclimation processes, to cope with frost exposure during winter and to restore growth ability in spring. Carbon metabolism is a key component of these processes through the osmotic control of extracellular ice formation and the trophic control of bud growth. The influence of temperature on the inter-conversion between starch and soluble carbohydrate has been evidenced for years, but we are currently missing an operational tool to predict starch vs. soluble carbohydrate contents during this period, which should allow to better predict frost hardiness. For this purpose, we exposed 1-year-old branches of Juglans regia to constant temperature for one to 3 weeks and measured the changes in carbohydrate composition at three periods (autumn, winter, and spring). As expected, the temperature significantly affected the changes in carbohydrate composition, but the water content and the sampling period were also relevant. Higher starch hydrolysis was observed at low temperature (<5°C) for all sampling periods. Starch hydrolysis was also observed at warm temperature, but in autumn only. These data were used to compare three modeling approaches simulating the changes in carbohydrate composition through enzymatic analogy. The most empirical and the most mechanistic approach did not succeed to simulate external observations (Root Mean Standard Error of Prediction (RMSEP) > 30 mg.g DM−1, Efficiency (Eff) <0), whereas the intermediate model was more efficient (RMSEP = 15.19 mg.g DM−1, Eff = 0.205 and 16.61 mg.g DM−1, Eff = 0.366, for GFS (Glucose + Fructose + Sucrose) and starch, respectively). The accuracy of the model was further improved when using field data for calibration (RMSEP = 5.86 mg.g DM−1, Eff = 0.962; RMSEP = 10.56 mg.g DM−1, Eff = 0.752, for GFS and starch, respectively). This study provided an operative tool to simulate carbohydrate dynamics over leafless period that could predict frost hardiness with approx. 3.4°C accuracy with temperature, water content and initial starch and soluble carbohydrate measurements. It should now be tested under various meteorological conditions and biological systems.

Quantitative evaluation of vitrinite reflectance and atomic O/C in coal using Raman spectroscopy and multivariate analysis

Vitrinite reflectance (VRo) is a standard petrographic method for assessing thermal maturity (rank) of coal. The vitrinite reflectance technique, however, requires significant petrographic experience, can be time-consuming, and may be biased by analyst subjectivity. Correlations between coal rank and Raman spectral properties are a promising alternative that can supplant some of the limitations inherent in the VRo protocol. The traditional peak-fitting methodologies for quantifying metrics from Raman spectra, however, also suffer from analyst subjectivity that can affect correlations between analyte and spectral properties.This research combines high-throughput Raman spectroscopy with multivariate analysis (MVA) to create calibration models for the prediction of coal rank though VRo and atomic O/C ratio. MVA techniques eliminate the ambiguous subjectivity prevalent in peak-fitting methods by evaluating the full Raman spectrum, then identifying the integral vibrational modes for constructing accurate models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.23–5.23%) for 68 geographically diverse coal samples. The calibration set was validated using one-half of the samples to rigorously assess the model’s predictive accuracy. The root mean standard error of prediction was 0.19 for the VRo model and 0.014 for the atomic O/C model. Both models exhibited linear correlations, with coefficients of determination (R2) for the validation set of 0.99 (VRo) and 0.93 (atomic O/C), despite the geographic and rank diversity of the samples. This study demonstrates the applicability and power of using PLS models for the prediction of both the VRo and atomic O/C ratio from Raman spectra. The quantitative MVA protocol contained herein provides a Raman alternative to the VRo industry benchmark for coal rank that is not subject to the limitations and subjectivity of peak-fitting methods.

Test of sampling methods to optimize the calibration of vine water status spatial models

Plant water status is one of the main factors affecting yield and quality in viticulture. Nevertheless, it is generally difficult to characterize it with enough precision for management purposes. In addition to its temporal variation, related to climate conditions, it has been shown that it is also spatially variable within the vineyard. In practical terms, this makes traditional reference measurements both too costly and time consuming to be affordable. In contrast, it has been shown that spatial variation of plant water status can be inferred from more accessible information, such as plant vigour in Mediterranean conditions. The main practical limitation for this approach is that the relationship between vigour measurements and plant water status is specific for each block and needs to be explicitly calibrated. Furthermore, a high number of measurements are usually required for this calibration. The objective of this work was to propose and test sampling methods to optimize the calibration of a specific spatial model of vine water status using the minimum number of measurements. Two model-based sampling methods commonly used in non-spatial modelling, Kennard and Stone (K&S) and Surface Response (SR) were considered, tested and discussed. Satisfactory results were obtained with both methods: with a sample size of 9 calibration sites, both sampling methods gave similar errors to the reference model (Root Mean Standard Error of Prediction, RMSEP = 0.1 MPa), which was calibrated with 49 sites. Taking into consideration the advantages and limitations of each method, K&S is considered to be better adapted for the case study presented. The proposed sampling approach could be extended to other spatial models used in precision agriculture in which ancillary variables can be used to explain most of the spatial variation for any agronomic information of interest.

Assessment of Robustness for a Near-Infrared Concentration Model for Real-Time Release Testing in a Continuous Manufacturing Process

This study describes the robustness test of a transmission near-infrared spectroscopic (NIRS) model for prediction of drug concentration in core tablets as part of real-time release testing (RTRt) efforts for a continuous manufacturing process. Seven calibration blend samples were prepared spanning a concentration range from 70 to 130% of the active pharmaceutical ingredient (API) target level with tablets prepared with laboratory-scale equipment, and their NIR spectra were obtained to develop a partial least squares (PLS) calibration model. The calibration model’s accuracy was assessed with an independent set of tablets obtaining a root mean standard error of prediction (RMSEP) of less than 1.5% label claim at each concentration level. However, a decrease in the prediction of API concentration of tablets exposed to the production environment was observed indicating lack of robustness as a RTRt method. The API is known to interchange solvate molecules with water molecules from the environment. Even though this exchange does not affect product quality, it affects the NIR spectra. The calibration model was then optimized by excluding O–H bands from the spectral range and shown to predict accurately the drug concentration in tablets for up to 113 h after tablet compaction. The calibration model was updated by including tablets produced in the continuous manufacturing line within the calibration model. The updated calibration model was used to predict tablets from three different continuous manufacturing runs, involving different lots of excipients and drug. The results from the updated calibration model showed excellent agreement with reference HPLC results.

Quantitative Determination of Fluorine Content in Blends of Polylactide (PLA)-Talc Using Near Infrared Spectroscopy.

Near-infrared spectroscopy (NIRS) has been widely used for quantitative and/or qualitative determination of a wide range of matrices. The objective of this study was to develop a NIRS method for the quantitative determination of fluorine content in polylactide (PLA)-talc blends. A blending profile was obtained by mixing different amounts of PLA granules and talc powder. The calibration model was built correlating wet chemical data (alkali digestion method) and NIR spectra. Using FT (Fourier Transform)-NIR technique, a Partial Least Squares (PLS) regression model was set-up, in a concentration interval of 0 ppm of pure PLA to 800 ppm of pure talc. Fluorine content prediction (R2cal = 0.9498; standard error of calibration, SEC = 34.77; standard error of cross-validation, SECV = 46.94) was then externally validated by means of a further 15 independent samples (R2EX.V = 0.8955; root mean standard error of prediction, RMSEP = 61.08). A positive relationship between an inorganic component as fluorine and NIR signal has been evidenced, and used to obtain quantitative analytical information from the spectra.

Total Anthocyanin Content in Intact Açaí (Euterpe oleracea Mart.) and Juçara (Euterpe edulis Mart.) Fruit Predicted by Near Infrared Spectroscopy

Açaí (Euterpe oleraceae Mart.) and juçara (Euterpe edulis Mart.) palms are native to Brazil and these species are rich in anthocynanins. The methods applied to determine anthocyanins are time-consuming, generate chemical residues, and do not fit in modern on-line grading machines. As near infrared (NIR) spectroscopy has been used as a nondestructive method to determine anthocyanin, the objective of this study was to use NIR spectroscopy to predict total anthocyanin (TA) in intact açaí and juçara fruits. Spectra were collected using a Fourier transform (FT)-NIR spectrophotometer in the diffuse reflectance (4,000–10,000 cm−1) and TA reference data were obtained using the Association of Official Analytical Chemists (AOAC) method. Different treatments were applied to spectra and spectral data sets were correlated with TA by using partial least squares (PLSs) regression algorithm. The global-PLS model obtained with açaí and juçara spectra resulted in a root mean standard error of prediction (RMSEP) of 10.05 g·kg−1. However, this model was not adequate for TA levels found in açaí fruits, therefore individual models were developed. The açaí-PLS model proved to be more adequate, as RMSEP was reduced to 3.56 g·kg−1. On the other hand, the RMSEP obtained with the juçara-PLS model (6.59 g·kg−1) was almost the same of the global model. NIR spectroscopy can be used to adequately predict TA content in intact açaí and juçara fruits and this method could be used as an analytical procedure to monitor their quality.

Robust Ultraviolet-Visible (UV-Vis) Partial Least-Squares (PLS) Models for Tannin Quantification in Red Wine.

The validation of ultraviolet-visible (UV-vis) spectroscopy combined with partial least-squares (PLS) regression to quantify red wine tannins is reported. The methylcellulose precipitable (MCP) tannin assay and the bovine serum albumin (BSA) tannin assay were used as reference methods. To take the high variability of wine tannins into account when the calibration models were built, a diverse data set was collected from samples of South African red wines that consisted of 18 different cultivars, from regions spanning the wine grape-growing areas of South Africa with their various sites, climates, and soils, ranging in vintage from 2000 to 2012. A total of 240 wine samples were analyzed, and these were divided into a calibration set (n = 120) and a validation set (n = 120) to evaluate the predictive ability of the models. To test the robustness of the PLS calibration models, the predictive ability of the classifying variables cultivar, vintage year, and experimental versus commercial wines was also tested. In general, the statistics obtained when BSA was used as a reference method were slightly better than those obtained with MCP. Despite this, the MCP tannin assay should also be considered as a valid reference method for developing PLS calibrations. The best calibration statistics for the prediction of new samples were coefficient of correlation (R2val) = 0.89, root mean standard error of prediction (RMSEP) = 0.16, and residual predictive deviation (RPD) = 3.49 for MCP and R2val = 0.93, RMSEP = 0.08, and RPD = 4.07 for BSA, when only the UV region (260-310 nm) was selected, which also led to a faster analysis time. In addition, a difference in the results obtained when the predictive ability of the classifying variables vintage, cultivar, or commercial versus experimental wines was studied suggests that tannin composition is highly affected by many factors. This study also discusses the correlations in tannin values between the methylcellulose and protein precipitation methods.

A Simple Photometer as a Helping Device in Measuring Blood Glucose

1024x768 0 0 1 170 970 farradilaiskandar@ymail.com 8 2 1138 14.0 Normal 0 false false false EN-US JA X-NONE Measurement of non-invasive blood glucose is one way to increase the frequency of self-monitoring of blood glucose (SMBG). For NIR reflectance spectroscopy, its application in non-invasive constrained by high value of standard error of prediction. The mean standard error of prediction was 25 mg/dL. Theoretically, NIR reflectance spectroscopy still can be used to predict blood glucose levels in certain conditions such as hypoglycemia ( 225 mg/dL), which the difference between the three conditions is more than 25 mg/dL. The results showed that there are significant differences in standards values of photometer measurement between controlled FBG and hyperglycemic conditions (P = 0.002). The results also showed that the photometer can be used to assist the monitoring of blood glucose in FBG under control and hyperglycemic conditions. It can be seen from the average percentage of the daily controlled FBG conditions in patients conducting SMBG in photometer-assisted compared to in patients only use SMBG once a day (28% versus 18%, P = 0.344). Normal 0 false false false EN-US X-NONE X-NONE

Application of FT-NIR Spectroscopy to Apple Wine for Rapid Simultaneous Determination of Soluble Solids Content, pH, Total Acidity, and Total Ester Content

The feasibility of rapid simultaneous analysis of soluble solid content (SSC), PH, total acidity, and total ester content in apple wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least squares (PLS) models was studied in this work. A set of 380 apple wine samples were scanned in a quartz cuvette with a 1-mm path length in the NIR region (4,000–12,000 cm−1) in transmission mode. Calibration models were established by PLS with full cross-validation. The NIR spectra were pretreated with different methods, and the effective wave number range together with the number of PLS factors used in the model calibration was optimized. The optimal models yielded the determination coefficients of 0.91, 0.93, 0.98, and 0.92 and the root mean standard error of prediction (RMSEP) of 0.601, 0.077, 0.021, and 0.096 for SSC, pH, total acidity, and total ester content, respectively. This work demonstrated that NIR spectroscopy was a rapid and convenient technique for analyzing and predicting SSC, pH, total acidity, and total ester content simultaneously during apple wine fermentation process.

A novel multiple linear multivariate NIR calibration model‐based strategy for in‐line monitoring of continuous mixing

The capability of near infra‐red (NIR) spectroscopy to predict many different variables, such as concentration and humidity, has been demonstrated in many published works. Several of those articles have been in the subject of real time prediction of continuous operations. However, those demonstrations have been for narrow ranges of the variables, especially for powder concentration, which could present a nonlinear behavior of the NIR absorbance as a function of the entire range of concentration. This work developed a novel strategy to predict the entire range of powder concentration using multiple linear NIR calibration models. The root mean standard error of prediction and relative standard deviation (RSD) parameters were used to establish the number of the multiple linear calibration models; other statistical features were used to establish the correct prediction. It was found that a minimum number of linear partial least squares (PLS) calibration models were necessary to accurately predict the range from 0 to 100% w/w. This technique could also be used with other nonlinear behaviors. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3123–3132, 2014

Optimization of a Concanavalin A-Based Glucose Sensor Using Fluorescence Anisotropy

To date, the dependent nature of the recognition and transduction mechanisms in optical glucose sensors based upon Concanavalin A (ConA) has tended to prevent the sensors' full potential from being realized. In this paper, these mechanisms are independently optimized for a given assay configuration in order to decrease the predictive error of a ConA-based glucose sensor and to give a more accurate demonstration of its potential. To this end, we used fluorescence anisotropy as the transduction mechanism to determine the binding of ConA to 4 kDa FITC-dextran by measuring the change in the rotational correlation lifetime between the bound and unbound populations. By tracking the fluorescence anisotropy of this ligand, the ranges of ConA and 4 kDa FITC-dextran concentrations capable of being explored were not limited by the transduction mechanism. Using predetermined association constants, the binding responses to physiological glucose concentrations were predicted for different assay configurations, and experimentally collected fluorescence anisotropy data displayed the predicted trends for these assay configurations. From the experimental results, a calibration fit was generated for the optimized assay configuration to predict the glucose concentrations using the fluorescence anisotropy. This optimized assay displayed a mean standard error of prediction of 7.5 mg/dL (0-300 mg/dL), and 100% of the data points fell within clinically acceptable zones (A and B) upon the Clarke Error Grid Analysis. This indicates that, by independently optimizing the recognition and transduction mechanisms for the final assay configuration, the sensitivity of a competitive binding chemistry using ConA can be appropriately configured for continuous glucose monitoring applications.