Good Model Calibration Research Articles

External validation and adaptation of a dynamic prediction model for patients with high-grade extremity soft tissue sarcoma.

Background and ObjectivesA dynamic prediction model for patients with soft tissue sarcoma of the extremities was previously developed to predict updated overall survival probabilities throughout patient follow‐up. This study updates and externally validates the dynamic model.MethodsData from 3826 patients with high‐grade extremity soft tissue sarcoma, treated surgically with curative intent were used to update the dynamic PERsonalised SARcoma Care (PERSARC) model. Patients were added to the model development cohort and grade was included in the model. External validation was performed with data from 1111 patients treated at a single tertiary center.ResultsCalibration plots show good model calibration. Dynamic C‐indices suggest that the model can discriminate between high‐ and low‐risk patients. The dynamic C‐indices at 0, 1, 2, 3, 4, and 5 years after surgery were equal to 0.697, 0.790, 0.822, 0.818, 0.812, and 0.827, respectively.ConclusionResults from the external validation show that the dynamic PERSARC model is reliable in predicting the probability of surviving an additional 5 years from a specific prediction time point during follow‐up. The model combines patient‐, treatment‐specific and time‐dependent variables such as local recurrence and distant metastasis to provide accurate survival predictions throughout follow‐up and is available through the PERSARC app.

Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data

Abstract Spatial biases are a common feature of presence–absence data from citizen scientists. Spatial thinning can mitigate errors in species distribution models (SDMs) that use these data. When detections or non‐detections are rare, however, SDMs may suffer from class imbalance or low sample size of the minority (i.e. rarer) class. Poor predictions can result, the severity of which may vary by modelling technique. To explore the consequences of spatial bias and class imbalance in presence–absence data, we used eBird citizen science data for 102 bird species from the northeastern USA to compare spatial thinning, class balancing and majority‐only thinning (i.e. retaining all samples of the minority class). We created SDMs using two parametric or semi‐parametric techniques (generalized linear models and generalized additive models) and two machine learning techniques (random forest and boosted regression trees). We tested the predictive abilities of these SDMs using an independent and systematically collected reference dataset with a combination of discrimination (area under the receiver operator characteristic curve; true skill statistic; area under the precision‐recall curve) and calibration (Brier score; Cohen's kappa) metrics. We found large variation in SDM performance depending on thinning and balancing decisions. Across all species, there was no single best approach, with the optimal choice of thinning and/or balancing depending on modelling technique, performance metric and the baseline sample prevalence of species in the data. Spatially thinning all the data was often a poor approach, especially for species with baseline sample prevalence <0.1. For most of these rare species, balancing classes improved model discrimination between presence and absence classes using machine learning techniques, but typically hindered model calibration. Baseline sample prevalence, sample size, modelling approach and the intended application of SDM output—whether discrimination or calibration—should guide decisions about how to thin or balance data, given the considerable influence of these methodological choices on SDM performance. For prognostic applications requiring good model calibration (vis‐à‐vis discrimination), the match between sample prevalence and true species prevalence may be the overriding feature and warrants further investigation.

Predicting the chance on live birth per cycle at each step of the IVF journey: external validation and update of the van Loendersloot multivariable prognostic model

ObjectiveTo study the performance of the ‘van Loendersloot’ prognostic model for our clinic’s in vitro fertilisation (IVF) in its original version, the refitted version and in an adapted version replacing...

Calibration transfer for bioprocess Raman monitoring using Kennard Stone piecewise direct standardization and multivariate algorithms

AbstractIn the biopharmaceutical industry, Raman spectroscopy is now a proven PAT tool that enables in‐line simultaneous monitoring of several CPPs and CQAs in real‐time. However, as Raman monitoring requires multivariate modeling, variabilities unknown by models can impact the monitoring prediction accuracy. With the widespread use of Raman PAT tools, it is necessary to fix instrumental variability impacts, encountered for instance during a device replacement. In this work, we investigated the impact of instrumental variability between probes inside a multi‐channel analyzer and between two analyzers, and explored solutions to correct them on model prediction errors in cell cultures. It is shown that the Kennard Stone piecewise direct standardization (KS PDS) method enables to lower model prediction errors between probes of a multi‐channel analyzer from 20% to 10% on the cell densities (TCD/VCD). Considering the integration of a new device or the replacement of a previous one, it has been determined that a first cell culture monitoring can be directly performed with the new analyzer calibrated by the KS PDS method based on the dataset from the previous analyzer, with an accuracy better than 10% on the main components of the culture like glucose, lactate, and the cell densities. Then, the new data obtained by the new analyzer can be inserted in a global calibration dataset to integrate instrumental variability in the chemometric model: it is shown that only one batch with the new device in a consistent and equilibrated calibration dataset was sufficient to correct the prediction gap induced by instrumental variability, allowing to exploit the data from previous analyzers considering optimized methods. This methodology provides good multivariate calibration model prediction errors throughout the instrumental changes which is a requirement for model maintenance.

Predicting the Electric Conductivity and Potassium Leaching of Coffee by NIR Spectroscopy Technique

This study aimed to investigate the relationship between the experimental values of electrical conductivity and potassium leaching and near-infrared (NIR) spectra of green coffee beans. Analyses of electrical conductivity and potassium leaching were performed on two hundred fifty samples of green coffee beans. The near-infrared spectra of these samples were collected. The spectra were subjected to pretreatments of multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative, and second derivative. The multivariate statistical method of partial least square (PLS) regression was used in the elaboration of calibration models, which were later applied to forecast external samples. The results obtained demonstrate that good calibration models were developed. The best model obtained for estimating electrical conductivity showed a correlation of 0.97, while for potassium leaching, the maximum correlation between experimental and predicted values was 0.88. These results demonstrate good prospects for predicting the coffee bean cell membranes integrity through spectroscopic techniques.

Surface enhanced infrared absorption spectroscopy (SEIRA) as a green analytical chemistry approach: Coating of recycled aluminum TLC sheets with citrate capped silver nanoparticles for chemometric quantitative analysis of ternary mixtures as a green alternative to the traditional methods

The past two decades have seen the full expansion of all fields of Nanotechnology, Chemometrics, Recycling, and Vibration spectroscopy into most of the research areas. The proposed method involves the harmonization of the previously mentioned fields as a vital tool to fulfill the concepts of sustainability and green analytical chemistry. This may reduce the negative impact of analytical laboratory activities on the surrounding environment and enables the implementation of sustainable development principles to analytical laboratories. This work compares the performance of surface enhanced infrared spectroscopy (SEIRA) with traditional chromatographic techniques for quantification of active pharmaceutical ingredients concerning the twelve principles of green analytical chemistry. The used aluminum TLC slides were recycled to be used as a SEIRA substrate. Citrate capped silver nanoparticles were synthesized via one step chemical reduction method, characterized, and deposited on the surface of the recycled aluminum TLC slides to be used as an active mid-infrared surface for quantification of the active pharmaceutical ingredient’s combinations. SEIRA coupled with PLSR chemometric tool was developed, validated and successfully applied to mixtures having diverged concentration ranges (5, 30 and 500 μg ml−1) of Pholcodine, Pseudoephedrine and Paracetamol, respectively. Pholcodine is a synthetic or semi-synthetic opium alkaloid that derived from morphine. Pseudoephedrine is a sympathomimetic agent that is prohibited in sports competitions by the world antidoping agency at certain concentration levels. Paracetamol has analgesic and antipyretic actions. After optimization of the method parameters and number of latent factors, a good linear calibration model of the PLSR strategy was obtained as indicated by the lowest root mean square error of calibration and prediction obtained and the regression coefficients R2 of 0.9912, 0.9888, and 0.9992, respectively. The calibration ranges for the three drugs in their pharmaceutical combinations was 2.5–12.5, 15–75 and 200–600 μg ml−1, respectively. The method showed high resolving power for the three drugs in presence of excipients and good recoveries were obtained in a range of 97–102% with relative standard deviation < 2. The developed lab on a chip SEIRA analyzer in comparison to the traditional chromatographic techniques does not only fulfill the twelve principles of GAC but also it combines the merits of high throughput straightforward fingerprint analyzers, portable to measure samples on its place, cost effective, reduced sample volume and solvent consumption, coupled with intelligent chemometric tools to analyze multiple samples, reduced trials and time to get results.

P953Comparison of the original and updated ACTION risk scores for predicting in-hospital and one-year mortality in patients with acute myocardial infarction undergoing primary PCI

Abstract Background The Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry-Get With The Guidelines (GWTG) AMI mortality model and risk score (ACTION) were introduced in 2011 to predict in-hospital mortality. In 2016 score was updated to enable a more accurate assessment, but, up-to-date, external validation in direct comparison was not performed. Purpose We aimed to externally validate and compare the prognostic value of original and updated ACTION score for in-hospital and one-year mortality. Method From a prospective electronic registry of a high-volume catheterization laboratory in a period from January 2009 to December 2017, a total of 5615 consecutive patients who underwent pPCI were available for analysis. For each patient, original (O-) and updated (U-) ACTION scores were calculated using required clinical and angiographic characteristics. In-hospital and one-year mortality (follow-up available for 91%) were assessed. Calibration and discrimination of the three risk models were evaluated by the Hosmer-Lemeshow (H-L) goodness-of-fit test and C-statistic, respectively. Results Mortality rates for in-hospital and one-year mortality were 4.2% and 9.6%, respectively. Both scores showed good model calibration as assessed by the H-L test and very good discriminative power for in-hospital and one-year mortality as assessed by C-statistics (Table 1 &amp; Figure 1). Net reclassification index (NRI=1.06) showed that 48% of patients with in-hospital event and 58% without event, had their risk recalculated with U-ACTION with Integrated Discrimination Improvement slope 9.1% higher than in first model. Table 1 Risk score H-L H-L p value AUC 95% CI p value AUC 95% CI Significant p value O-ACTION 9.4 0.3 0.829 0.819 to 0.839 p&lt;0.0001 0.781 0.769 to 0.792 p&lt;0.0001 U-ACTION 10.9 0.2 0.918 0.911 to 0.925 0.838 0.827 to 0.848 Figure 1 Conclusion Updated ACTION score enables better prediction of in-hospital and one-year mortality in patients undergoing pPCI for acute myocardial infarction, thus it can be used preferentially over the original ACTION score for assessment of short and long-term mortality risks of this population.

P4619Comparison of the CRUSADE, ACUITY-HORIZONS, and ACTION bleeding risk scores for predicting in-hospital bleeding in acute myocardial infarction patients undergoing primary PCI

Abstract Background Considering clinical importance of bleeding complications in patients with acute myocardial infarction (AMI), bleeding risk stratification is a key part of the management of these patients. CRUSADE, ACTION and ACUITY-HORIZONS bleeding risk scores are available for predicting in-hospital major bleeding events in patients with acute myocardial infarction. Purpose We aimed to evaluate performance of the three above mentioned risk scores for predicting in-hospital bleeding events defined according to The Bleeding Academic Research Consortium (BARC) criteria. Methods From a prospective electronic registry of a high-volume catheterization laboratory in a period from January 2009 to December 2017, a total of 6505 consecutive patients with acute myocardial infarction who underwent pPCI were included in analysis. Calibration and discrimination of the three risk models were evaluated by the Hosmer-Lemeshow (H-L) goodness-of-fit test and C-statistic, respectively. Results Overall there were 372 (5.7%) bleeding events out of which 117 (1.8%) fulfilled stage BARC 3 or higher bleeding criteria. All three scores showed good model calibration as assessed by the H-Ls test and very good discriminative power for BARC 3 of higher bleeding events detection as assessed by C-statistics (Table 1 &amp; Figure 1): Bleeding events stage BARC 3 or higher were statistically highly related with higher in-hospital mortality (13.7% vs. 3.5%; p&lt;0.000). Table 1 Risk score H-L H-L p AUC 95% CI p CRUSADE 11.46 0.177 0.761 0.750–0.771 vs. ACUITY = ns vs. ACTION &lt;0.000 ACUITY-HORIZONS 10.47 0.236 0735 0.724–0.745 vs. ACTION = ns ACTION 5.74 0.677 0.701 0.698–0.712 Figure 1 Conclusions All three evaluated scores showed very good discriminative capacity for predicting BARC 3 or higher bleeding events in patients undergoing pPCI for AMI.

Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study.

The greater the severity of illness of a patient, the more likely the patient will have a poor hospital outcome. However, hospital-wide severity of illness scores that are simple, widely available, and not diagnosis-specific are still needed. Laboratory tests could potentially be used as an alternative to estimate severity of illness. To evaluate the ability of hospital laboratory tests, as measures of severity of illness, to predict in-hospital mortality among hospitalized patients, and therefore, their potential as an alternative method to severity of illness risk adjustment. A retrospective cohort study among 38,367 adult non-trauma patients admitted to the University of Maryland Medical Center between November 2015 and November 2017 was performed. Laboratory tests (hemoglobin, platelet count, white blood cell count, urea nitrogen, creatinine, glucose, sodium, potassium, and total bicarbonate (HCO3)) were included when ordered within 24h from the time of hospital admission. A multivariable logistic regression model to predict in-hospital mortality was constructed using a section of our cohort (n = 21,003). Model performance was evaluated using the c-statistic and the Hosmer-Lemeshow (HL) test. In addition, a calibration belt was constructed to determine a confidence interval around the calibration curve with the purpose of identifying ranges of miscalibration. Patient age and all laboratory tests predicted mortality with good discrimination (c = 0.79). Patients with abnormal HCO3 levels or leukocyte counts at admission were twice as likely to die during their hospital stay as patients with normal results. A good model calibration and fit were observed (HL = 13.9, p = 0.18). Admission laboratory tests are able to predict in-hospital mortality with good accuracy, providing an objective and widely accessible approach to severity of illness risk adjustment.

RAPID AUTHENTICATION OF TURMERIC POWDER ADULTERATED WITH CURCUMA ZEDOARIA AND CURCUMA XANTHORRHIZA USING FTIR-ATR SPECTROSCOPY AND CHEMOMETRICS

Objective: The objective of this study is to develop a rapid, simple, non-destructive and inexpensive analytical method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) as a sampling technique, combined with chemometrics for authentication of turmeric powder adulterated with Curcuma zedoaria and Curcuma xanthorrhiza.&#x0D; Methods: Turmeric powder is placed above the diamond crystal in ATR compartment. Spectra are scanned in the absorbance mode from 4000 to 600 cm-1. The obtained spectra is further analyzed by Principal Component Analysis (PCA), Partial Least Square Discriminant Analysis (PLS-DA), and Partial Least Square Regression (PLS-R).&#x0D; Results: PCA score plot shows that Curcuma longa, Curcuma zedoaria, and Curcuma xanthorrhiza can be discriminated well. PLS-DA can be used to build the model for classification between pure turmeric powder and adulterated powder with the values of Q2, R2X, and R2Y of 0.9558, 0.9813, and 0.9746, respectively. The good calibration model for quantification of each adulterant is obtained by PLS-R with R2 value more than 0.99 and lower RMSEC value. Both models have been validated by internal and external validation which result in the high R2 value and low RMSEP value which indicates that both models are accurate and precise.&#x0D; Conclusion: The combination of FTIR-ATR spectroscopy and chemometrics can be used to authenticate turmeric powder adulterated with Curcuma zedoaria and Curcuma xanthorrhiza.

Predicting reamputation risk in patients undergoing lower extremity amputation due to the complications of peripheral artery disease and/or diabetes.

Patients undergoing amputation of the lower extremity for the complications of peripheral artery disease and/or diabetes are at risk of treatment failure and the need for reamputation at a higher level. The aim of this study was to develop a patient-specific reamputation risk prediction model. Patients with incident unilateral transmetatarsal, transtibial or transfemoral amputation between 2004 and 2014 secondary to diabetes and/or peripheral artery disease, and who survived 12 months after amputation, were identified using Veterans Health Administration databases. Procedure codes and natural language processing were used to define subsequent ipsilateral reamputation at the same or higher level. Stepdown logistic regression was used to develop the prediction model. It was then evaluated for calibration and discrimination by evaluating the goodness of fit, area under the receiver operating characteristic curve (AUC) and discrimination slope. Some 5260 patients were identified, of whom 1283 (24·4 per cent) underwent ipsilateral reamputation in the 12 months after initial amputation. Crude reamputation risks were 40·3, 25·9 and 9·7 per cent in the transmetatarsal, transtibial and transfemoral groups respectively. The final prediction model included 11 predictors (amputation level, sex, smoking, alcohol, rest pain, use of outpatient anticoagulants, diabetes, chronic obstructive pulmonary disease, white blood cell count, kidney failure and previous revascularization), along with four interaction terms. Evaluation of the prediction characteristics indicated good model calibration with goodness-of-fit testing, good discrimination (AUC 0·72) and a discrimination slope of 11·2 per cent. A prediction model was developed to calculate individual risk of primary healing failure and the need for reamputation surgery at each amputation level. This model may assist clinical decision-making regarding amputation-level selection.

Cohort-level disease prediction using aggregate biomarker data measured at dry-off in transition dairy cattle: A proof-of-concept study

During the transition from late gestation to early lactation, dairy cattle are at increased risk for disease. Herd-level monitoring for disease risk involves evaluating multiple factors, including food intake, cow density, and biomarkers of nutrient metabolism. Biomarkers that are measured include non-esterified fatty acids (NEFA) and beta-hydroxybutyrate (BHB), which are usually measured in a subset of the herd (i.e. cohort). If a certain proportion of cows in the cohort are above a specific threshold for a biomarker, the cohort is considered at high risk of disease. Few previous studies have investigated other methods to aggregate individual cow-level data to the cohort level. We designed a proof-of-concept study to determine if biomarker aggregation methods may be useful to predict cohort incidence of adverse health events including 1) clinical diseases: mastitis, metritis, retained placenta, ketosis, lameness, pneumonia, milk fever, displaced abomasum, 2) and abortion or death of the calf or the cow. The study design was a prospective cohort study that used cows (N = 277) from five Michigan commercial dairy herds. Multiple cohorts of cows (two to four cohorts per farm, 18 total) were enrolled that shared the same dry-off date. We tested three different methods (central, dispersion, and count) to aggregate individual cow data (i.e. biomarkers and covariates) measured at dry-off. The central method consisted of calculating the average value of each variable within a cohort, and the dispersion method involved taking the standard deviation or mean absolute deviation about the median of each variable within a cohort. The count method consisting of counting the number of cows above a specific threshold for each variable within a cohort. We used best subsets selection to select a bouquet of candidate models for each aggregation method and averaged the predictions over the model set. We built 4 sets of Poisson regression models: one for each aggregation method and a combined model that included all three methods. We evaluated the models based on goodness-of-fit, model calibration using scoring rules, and comparison of observed versus predicted counts. The central and the combined method produced models that had good fit and model calibration. These results indicate that it may be possible to use aggregate measures to predict cohort disease incidence as early as dry-off. The next step is to test biomarker aggregation methods in studies with larger sample sizes.

Accuracy of Predicting the Moisture Content of Three Types of Wood Sections Using Near Infrared Spectroscopy

The moisture content of wood affects its physical properties. Many researchers have attempted to measure wood moisture contents nondestructively using near-infrared (NIR) spectroscopy. Wood is a natural material with anisotropic characteristics. There are three section types of cut wood surfaces, namely cross, radial, and tangential sections. In this study the NIR spectra of all three types of wood sections were measured and compared to determine how accurately the water content of softwood could be predicted. Two wood species were chosen and were cut into 30-mm cubes. The wood samples were stored for two weeks under various humidity conditions before they underwent NIR spectroscopy. The NIR spectra were obtained from three wood sections and principal component regression (PCR) was performed. Good calibration models for the three types of sections were then obtained. Furthermore, to expand the application of the PCR model for each section type, combinations of calibration models and prediction sets of the other sections were implemented. For the cross section models, there was no clear prediction capability when the test sets from the other two section types were used. However, for the radial and tangential sections, a high prediction accuracy was obtained using the other test set.

Understanding the Value of Individualized Information: The Impact of Poor Calibration or Discrimination in Outcome Prediction Models.

Risk prediction models allow for the incorporation of individualized risk and clinical effectiveness information to identify patients for whom therapy is most appropriate and cost-effective. This approach has the potential to identify inefficient (or harmful) care in subgroups at different risks, even when the overall results appear favorable. Here, we explore the value of personalized risk information and the factors that influence it. Using an expected value of individualized care (EVIC) framework, which monetizes the value of customizing care, we developed a general approach to calculate individualized incremental cost effectiveness ratios (ICERs) as a function of individual outcome risk. For a case study (tPA v. streptokinase to treat possible myocardial infarction), we used a simulation to explore how an EVIC is influenced by population outcome prevalence, model discrimination (c-statistic) and calibration, and willingness-to-pay (WTP) thresholds. In our simulations, for well-calibrated models, which do not over- or underestimate predicted v. observed event risk, the EVIC ranged from $0 to $700 per person, with better discrimination (higher c-statistic values) yielding progressively higher EVIC values. For miscalibrated models, the EVIC ranged from -$600 to $600 in different simulated scenarios. The EVIC values decreased as discrimination improved from a c-statistic of 0.5 to 0.6, before becoming positive as the c-statistic reached values of ~0.8. Individualizing treatment decisions using risk may produce substantial value but also has the potential for net harm. Good model calibration ensures a non-negative EVIC. Improvements in discrimination generally increase the EVIC; however, when models are miscalibrated, greater discriminating power can paradoxically reduce the EVIC under some circumstances.

Predicting the prevalence of peripheral arterial diseases: modelling and validation in different cohorts.

To develop models for prevalence estimation of peripheral arterial disease (PAD) and to validate them in an external cohort. Model training cohort was a population based cross-sectional survey. Age, sex, smoking status, body mass index, total cholesterol (TC), high density lipoprotein (HDL), TC/HDL ratio, low density lipoprotein, fasting glucose, diabetes, hypertension, pulse pressure, and stroke history were considered candidate predicting variables. Ankle brachial index ≤ 0.9 was defined as the presence of peripheral arterial disease. Logistic regression method was used to build the prediction models. The likelihood ratio test was applied to select predicting variables. The bootstrap method was used for model internal validation. Model performance was validated in an external cohort. The final models included age, sex, pulse pressure, TC/HDL ratio, smoking status, diabetes, and stroke history. Area under receiver operating characteristics (AUC) with 95% confidence interval (CI) of the final model from the training cohort was 0.74 (0.70, 0.77). Model validation in another cohort revealed AUC (95% CI) of 0.72 (0.70, 0.73). P value of Hosmer-Lemeshow's model goodness of fit test was 0.75 indicating good model calibration. The developed model yielded a moderate usefulness for predicting the prevalence of PAD in general population.

In Situ Monitoring of Antisolvent Cocrystallization by Combining Near-Infrared and Raman Spectroscopies

In situ monitoring techniques are essential for the control and optimization of the cocrystallization process. In our previous study, we successfully monitored indomethacin–saccharin (IMC–SAC) cocrystallization by antisolvent addition using a method based on near-infrared principal component analysis (NIR–PCA). In this study, a calibration model was developed to predict the solute concentration of the two components. Several samples withdrawn from five sets of experiments were used to develop the calibration model. The actual concentrations of the two components were determined using UV–vis spectroscopy and high performance liquid chromatography (HPLC). The amount of solid-phase material in suspension was calculated from these solute concentration data. Correlations between NIR spectra and solid concentrations were evaluated using partial least-squares (PLS) regression analyses. Reasonably good calibration models with determination coefficients (R2) higher than 0.979 were obtained. Process monitoring was performed using in situ NIR and Raman spectroscopies to predict the concentrations of both IMC and SAC in solution and to identify the solid-phase materials, respectively. The calibration models were deemed suitable, with reasonable accuracy and precision, for in situ concentration monitoring of the antisolvent crystallization of IMC–SAC cocrystals. This combination of NIR and Raman spectroscopies was able to detect the formation and phase transition of the resulting cocrystal.

Multipoint near-infrared spectrometry for real-time monitoring of protein conformational stability in powdered infant formula

Powdered infant formula (PIF) can be the sole source of nutrition for babies and infants. Monitoring conformational changes in protein during manufacture of PIF is critical in order to maintain its nutritional value. This study presents the development of a calibration model for monitoring conformational changes in PIF protein by applying a novel multipoint near-infrared (NIR) spectrometry. NIR spectra were collected for PIF and PIF proteins, casein and whey protein isolate, before and after heat treatment. Results show that principal component analysis showed discrimination between native protein at room temperature and protein conformational changes caused at elevated temperature. Partial least squares regression analysis showed good calibration models with correlation coefficients ranging between 87% and 99% for the prediction of protein quality. This novel multipoint NIR spectrometry could serve as a simple in-line tool to rapidly monitor protein quality during processing stages, contributing to product nutritional value.

Analysis of carbonyl value of frying oil by fourier transform infrared spectroscopy.

A rapid method for determining the carbonyl value of frying oils has been developed using Fourier-transform infrared (FTIR) spectroscopy and chemometrics. One hundred and fifty-six frying oils with different carbonyl values were collected from an actual potato frying process. FTIR spectra in the range of 4000-650 cm(-1) were scanned with a FTIR spectroscopy apparatus using the attenuated total reflectance (ATR) method. A good calibration model was obtained using the partial least-squares (PLS) regression method with full cross validation for predicting the carbonyl value of frying oils. For the model, the coefficients of determination (R(2)), standard errors of cross validation (SECV) and standard errors of prediction (SEP) were 0.99, 1.87 μmol g(-1) and 1.93 μmol g(-1), respectively. Moreover, standard deviation ratios of reference data in the validation sample set to the SEP were higher than 3. This study shows that the carbonyl value of frying oils can be successfully determined to a high accuracy using FTIR spectroscopy combined with PLS regression.

In situ near infrared spectroscopy monitoring of cyprosin production by recombinant Saccharomyces cerevisiae strains

Near infrared (NIR) spectroscopy was used to in situ monitoring the cultivation of two recombinant Saccharomyces cerevisiae strains producing heterologous cyprosin B. NIR spectroscopy is a fast and non-destructive technique, that by being based on overtones and combinations of molecular vibrations requires chemometrics tools, such as partial least squares (PLS) regression models, to extract quantitative information concerning the variables of interest from the spectral data. In the present work, good PLS calibration models based on specific regions of the NIR spectral data were built for estimating the critical variables of the cyprosin production process: biomass concentration, cyprosin activity, cyprosin specific activity, the carbon sources glucose and galactose concentration and the by-products acetic acid and ethanol concentration. The PLS models developed are valid for both recombinant S. cerevisiae strains, presenting distinct cyprosin production capacities, and therefore can be used, not only for the real-time control of both processes, but also in optimization protocols. The PLS model for biomass yielded a R2=0.98 and a RMSEP=0.46gdcwl−1, representing an error of 4% for a calibration range between 0.44 and 13.75gdcwl−1. A R2=0.94 and a RMSEP=167Uml−1 were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0–2509Uml−1), whereas a R2=0.93 and RMSEP=672Umg−1 were obtained for the cyprosin specific activity, corresponding to an error of 7% of the experimental data range (0–11,690Umg−1). For the carbon sources glucose and galactose, a R2=0.96 and a RMSECV of 1.26 and 0.55gl−1, respectively, were obtained, showing high predictive capabilities within the range of 0–20gl−1. For the metabolites resulting from the cell growth, the PLS model for acetate was characterized by a R2=0.92 and a RMSEP=0.06gl−1, which corresponds to a 6.1% error within the range of 0.41–1.23gl−1; for the ethanol, a high accuracy PLS model with a R2=0.97 and a RMSEP=1.08gl−1 was obtained, representing an error of 9% within the range of 0.18–21.76gl−1. The present study shows that it is possible the in situ monitoring and prediction of the critical variables of the recombinant cyprosin B production process by NIR spectroscopy, which can be applied in process control in real-time and in optimization protocols. From the above, NIR spectroscopy appears as a valuable analytical tool for online monitoring of cultivation processes, in a fast, accurate and reproducible operation mode.

A Simple Nonexercise Model of Cardiorespiratory Fitness Predicts Long-Term Mortality

Cardiorespiratory fitness (CRF) is a strong predictor of future health, but measurements of CRF are time consuming and involve costly test procedures. We assessed whether a simple, non-exercise-based test of CRF predicted long-term all-cause and cardiovascular disease (CVD) mortality. In this prospective cohort study, we used a previously published nonexercise test to estimate CRF in healthy men (n = 18,348) and women (n = 18,764) from the first HUNT study (1984-1986) in Norway. We used Cox regression to obtain HR for mortality during a mean follow-up of 24 yr. Assessment of model validity was performed by standard procedures of discrimination and calibration. CRF was inversely associated with all-cause and CVD mortality in men and women below 60 yr of age at baseline, after adjustment for confounders. For each MET-higher CRF (MET, approximately 3.5 mL·kg·min), HR for CVD mortality was 21% lower in both men (95% confidence interval (CI), 17%-26%) and women (95% CI, 12%-29%). HR for all-cause mortality was 15% (95% CI, 12%-17%) lower in men and 8% (95% CI, 3%-3%) lower in women for each MET-higher CRF. The ability of the model to discriminate mortality risk among participants below 60 yr was better for CRF (area under the curve (AUC), 0.70-0.77) compared with that for each variable that constituted the model (AUC, 0.55-0.63) and an aggregated sum of z-scores for each variable (AUC, 0.61-0.65). Comparison of observed and predicted risk indicated good model calibration. This method of assessing CRF is feasible and practically useful in primary care for identification of apparently healthy individuals at increased risk of premature CVD disease and all-cause mortality.