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One of the main objectives of food safety programs is the assurance of freshness and quality as well as to prolong shelf life of food ingredients or products along the food supply and value chain. It is well recognized that shelf life prolongation could play a key role for reducing food waste but the real relationships between shelf life and food waste is not well understood due to the lack of data. Sensing technologies and data analytics have been suggested as potential tools to better monitor or predict the shelf life of fresh produces. These new technologies have produced changes in the way food composition, overall quality and shelf life are analysed or monitored, adding with new challenges for the food manufacturing industry. The advantages and limitations on the utilization of sensing technologies based on vibrational spectroscopy, including mid infrared (MIR), near infrared (NIR) and hyperspectral imaging as tools to assess and monitor shelf life in food produces and products is reviewed. The different studies evaluated concluded that the use of infrared (IR) sensors allow for the development of efficient and robust systems to monitor the safety and shelf life of fresh produces and food products. The use of IR sensors has shown the potential to develop management decision tools that would allow for the evaluation and monitoring of shelf life along the food supply and value chains. [Display omitted] • Sensing technologies have been suggested as tools to monitor shelf life of fresh produces. • The use of infrared (IR) sensors is efficient and robust to monitor shelf life of fresh produces. • Limitation and advantages of sensing technologies were discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • A portable near infrared (NIR) was evaluated to monitor shelf-life of four goat muscles. • The cross-validation statistics were obtained for the prediction of days of storage in the biceps femoris muscle. • Differences in the loadings for the cross-validation models were observed due to the interactions between the different muscle samples and days of storage. • These results showed the potential of NIR spectroscopy to identify the time of storage in four different goat muscles. The objective of this study was to evaluate the use of a portable near infrared (NIR) instrument to monitor the shelf-life of four goat muscles [ longissimus thoracis et lumborum (LTL), semimembranosus (SM), semitendinosus (ST) and biceps femoris (BF)] stored for up to 8 days (4 °C). The NIR spectra of the muscle samples were collected at day 0, and after 1, 4 and 8 days of storage using a MicroNIR instrument (900–1600 nm). The coefficient of determination in cross-validation (R2) and the standard error in cross validation (SECV) obtained for the prediction of days of storage ranged between 0.76 and 0.86, where the SECV ranged from 0.32 to 0.41. The best statistics in cross-validation were obtained for the prediction of days of storage in the BF samples, followed by the ST and LTL muscles. Differences in the PLS loadings for the cross-validation models were observed due to the interactions between the different muscle samples and days of storage. Overall, these results showed the potential of NIR spectroscopy to identify the time of storage in four different goat muscles. Similar data and techniques could be used to predict the remaining shelf life of meat derived from different species under storage. This information can then be used as a tool to predict and guarantee the safety of meat samples to the consumer along the meat supply and value chains. [ABSTRACT FROM AUTHOR]

Most of the published articles related to the application of near infrared (NIR) spectroscopy for the quantitative analysis of soils parameters, have reported experiments or studies that are largely laboratory based where soil properties such as soil organic carbon (SOC) content are assessed on dry and sieved samples. The recent arrival of hand-held and portable analytical equipment allowing for the quantitative analysis of samples in situ have provided new possibilities for the analysis of soils. The implementation of this type of instrumentation will result in reduced time and cost of analysis, and will have a profound effect on the associated method steps (e.g. storing, drying, and sieving) currently used when soils are analysed in the laboratory. The aim of this short review is to highlight the gaps in the research related to the analysis of soil samples by NIR spectroscopy with high moisture (HM) content. Inconsistent results were found in the scientific literature in relation with the ability of NIR spectroscopy to measure soil chemical properties with HM content. The use of pre-processing methods did not improve the calibrations nor the selection of samples with different moisture levels. The re-wetting (hydration) of the sample and the conversion of units into volumetric ones led to optimism for the measurement of in field samples by NIR spectroscopy. However, in field applications are still in their infancy. [ABSTRACT FROM AUTHOR]

In recent decades, research in barley has led to improvements in several biochemical and chemical characteristics (e.g. protein, starch, β glucans) associated with malt composition and end product quality (e.g. beer). Although lipids and fatty acids (FA) represent between 1 to 3% of the grain weight (dw), they can play an important role in barley grain and malt derived materials. However, the role of lipids and FA in both barley and malt quality is still not well understood. The aim of this article is to provide an overview on the role of lipids and FA in the chemical and industrial properties of barley grain, malt and wort. [ABSTRACT FROM AUTHOR]

Variations in the amount and rates of water uptake influence the seed hydration as well as the modification of the endosperm for industrial uses (e.g., malting). The aim of this study was to investigate and interpret absorption frequencies in the mid infrared (MIR) region associated with water uptake in whole seeds, husk and endosperm of barley seeds during the initial period of soaking in water. Partial least squares (PLS) regression models for the prediction of water uptake in the set of samples yield a coefficient of determination ( R 2 ) and a standard error in cross validation of 0.75 and 2.57 (% w/w), respectively. The biological implications of this study are that the first stages of germination can be monitored using the information derived from the MIR spectra. These results also demonstrated that whole seeds, endosperm and husk derived from the same variety or genotype have different patterns in the MIR region. [ABSTRACT FROM AUTHOR]

Highlights: [•] Attenuated total reflectance and mid infrared spectroscopy was used to understand gelatinization in whole barley samples. [•] The interpretation of the data generated was achieved by the use of MIR ratios and two dimensional spectroscopy. [•] The result of this study showed that ATR-MIR spectroscopy can be used as tool to interpret differences in barley samples. [Copyright &y& Elsevier]

Abstract: Although both near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy combined with multivariate data analysis (MVA) have been extensively used to measure chemical composition (e.g. protein, moisture, oil) in a wide number of grains few reports can be found on the use of this methods for varietal discrimination and traceability of cereals. In this overview applications of NIR spectroscopy and MIR spectroscopy combined with multivariate data methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA) to aid on the authentication and traceability of cereals are discussed. [Copyright &y& Elsevier]

Highlights: [•] Water uptake by the endosperm is an important property of barley for malting. [•] Fatty acids and total lipids have an important role on determining water uptake. [•] Unsaturated fatty acids such oleic acid have an effect on water uptake. [Copyright &y& Elsevier]

Highlights: [•] The combination of mid infrared spectroscopy and chemometrics was evaluated to measure pasting properties in barley. [•] The interpretation of the data generated allowed to identify regions from the MIR related to starch pasting properties. [•] The result of this study showed that ATR-MIR spectroscopy can be used to predict some starch pasting properties. [ABSTRACT FROM AUTHOR]

Abstract: Relatively few studies have examined the influence of lipids or fatty acids on starch pasting properties in barley. The aim of this study was to evaluate the use of the Rapid Visco Analyser (RVA) as a tool to examine changes in starch functionality related with free fatty acids (FFAs) and amylose content in barley flour samples. Changes in RVA parameters such as setback (STB) and final viscosity (FV) were related to differences in both FFA (oleic and palmitic acids) and amylose contents due to the diverse barley genotypes analysed. The magnitude of the RVA profile values derived from samples having high a concentration of palmitic acid (3900mg/g) was lower compared with that having the lower concentration of the same FFA (2470mg/g), displaying low FV (5666 vs 4166cP) and low STB (1625 vs 1174cP) values. Similar trends were observed for the other FFA measured. These results showed that pasting properties of barley measured using RVA are determined by a complexity of parameters, where the combination of lipids (FFA) and amylose might determine the characteristics of the material and its final use (e.g. malting). [Copyright &y& Elsevier]

Abstract: The aim of this study was to evaluate the use of near infrared reflectance (NIR) spectroscopy to monitor water uptake and steeping time in whole barley samples as a rapid and easy to use technique. Whole barley grain samples were steeped in water, and subsamples were analyzed for water uptake (gravimetric method) and using NIR spectroscopy. The spectra and the analytical data were used to develop partial least squares (PLS) calibrations to predict water uptake and steeping time. Cross validation models for water uptake and steeping time gave a coefficient of determination in cross validation (R 2) and the standard error of prediction (SEP) of 0.90 (SEP=5.36g/100g fw) and 0.92 (SEP=3.93h), respectively. This study showed that NIR spectroscopy combined with PLS regression showed promise as a rapid, non-destructive method to monitor and measure water uptake and steeping time in whole barley during soaking. [Copyright &y& Elsevier]

Abstract: Effective fermentation monitoring is a growing need during the production of wine due to the rapid pace of change in the industry, which calls for fast methods providing real-time information in order to reduce stuck and sluggish fermentations and to assure the quality of the product at all stages of the process. During wine fermentation it is important to measure both substrate and product concentrations (e.g. sugars, ethanol) and to evaluate other quality characteristics of the final product, such as the phenolic composition or volatile compounds. However, the analysis of these compounds by traditional methods requires sample preparation, specific analytical equipment and is time-consuming. Therefore real-time monitoring and control of the bioprocesses are necessary for increased productivity, efficiency, and reproducibility. The aim of this study is to evaluate the potential of attenuated total reflectance (ATR) mid infrared (MIR) spectroscopy to monitor wild ferments during wine production. The results obtained showed that it is possible to monitor the time course of fermentation in wild yeast using ATR-MIR spectroscopy. Partial least squares (PLS) regression models allowed to predict the time course of fermentation (standard error of prediction 1.2 days). [Copyright &y& Elsevier]

Abstract: The wine industry requires rapid, comprehensive methods and techniques to answer the new challenges driven by the market demands. Recent advances in spectroscopy technologies have brought about a revolution in the manner in which biological systems are visualised and analysed. The measurement of numerous small molecules (metabolites) metabolised by microorganisms during growth in wine, will benefit from techniques that require minimal sample preparation, permit the automatic analysis of many samples with negligible reagent costs, allow their rapid characterization against a stable database, and are easy to use. With recent developments in analytical instrumentation, these requirements are being fulfilled by vibrational spectroscopic methods, often referred to as “whole-organism fingerprinting” and more recently “metabolic fingerprinting”. The objective of this study was to evaluate and compare the use of near (NIR) and mid infrared (MIR) spectroscopy as rapid methods to distinguish red and white wines obtained by using different strains of Oenococcus oeni following malolactic fermentation. Using NIR and MIR wines produced with different O. oeni strains could be distinguished both in red and white wines yielding correct classification rates between 67 and 100% depending on the strain. [Copyright &y& Elsevier]

Abstract: The goal of building a multivariate calibration model is to predict a chemical or physical property from a set of predictor variables, for example the analysis of sugar concentration in fruits using near infrared (NIR) spectroscopy. Effective multivariate calibration models combined with a rapid analytical method should be able to replace laborious and costly reference methods. The quality of a calibration model primarily depends on its predictive ability. In order to build, interpret and apply NIR calibrations not only the quality of spectral data but also other properties such as effect of reference method, sample selection and interpretation of the model coefficients are also important. The objective of this short review is to highlight the different steps, methods and issues to consider when calibrations based on NIR spectra are developed for the measurement of chemical parameters in both fruits and fruit juices. The same principles described in this paper can be applied to other rapid methods like electronic noses, electronic tongues, and fluorescence spectroscopy. [Copyright &y& Elsevier]

Abstract: The combination of UV, visible (Vis), near-infrared (NIR) and mid-infrared (MIR) spectroscopy with multivariate data analysis was explored as a tool to classify commercial Sauvignon Blanc (Vitis vinifera L., var. Sauvignon Blanc) wines from Australia and New Zealand. Wines (n =64) were analysed in transmission using UV, Vis, NIR and MIR regions of the electromagnetic spectrum. Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) were used to classify Sauvignon Blanc wines according to their geographical origin using full cross validation (leave-one-out) as a validation method. Overall PLS-DA models correctly classified 86% of the wines from New Zealand and 73%, 86% and 93% of the Australian wines using NIR, MIR and the concatenation of NIR and MIR, respectively. Misclassified Australian wines were those sourced from the Adelaide Hills of South Australia. These results demonstrate the potential of combining spectroscopy with chemometrics data analysis techniques as a rapid method to classify Sauvignon Blanc wines according to their geographical origin. [ABSTRACT FROM AUTHOR]

Abstract: A simple and fast method was developed for simultaneously determining ethanol, specific gravity (SG), volatile acidity (VA), glucose plus fructose (G+F), pH and titratable acidity (TA) in commercial Australian red and white wine samples using mid-infrared (MIR) spectroscopy and attenuated total reflectance (ATR). Wine samples (n=130) were analysed using an MIR instrument equipped with a single bounce ATR cell. Results from this study demonstrated the capability of ATR-MIR coupled with partial least squares (PLS) regression to measure compositional parameters in wine. The standard errors of prediction (SEP) obtained were of 0.11 (%) for ethanol, 0.0007 for SG, 0.10 for pH, 0.53g/L for TA, 1.35g/L for G+F, and 0.12g/L for VA. Both the sample preparation time of analysis and volume of sample required were considerably reduced compared to the transmission MIR measurements currently used by the wine industry. [ABSTRACT FROM AUTHOR]

Abstract: Visible (VIS) and near infrared (NIR) spectroscopy combined with chemometrics was used in an attempt to classify commercial Riesling wines from different countries (Australia, New Zealand, France and Germany). Commercial Riesling wines (n =50) were scanned in the VIS and NIR regions (400–2500nm) in a monochromator instrument, in transmission mode. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) based on PCA scores were used to classify Riesling wines according to their country of origin. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. PLS-DA models correctly classified 97.5%, 80% and 70.5% of the Australian, New Zealand and European (France and Germany) Riesling wines, respectively. SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively. These results demonstrated that the VIS and NIR spectra contain information that when used with chemometrics allow discrimination between wines from different countries. To further validate the ability of VIS–NIR to classify white wine samples, a larger sample set will be required. [Copyright &y& Elsevier]

Abstract: The use of visible (VIS) and near infrared spectroscopy (NIRS) to measure the concentration of elements in Australian wines was investigated. Both white (n =32) and red (n =94) wine samples representing a wide range of varieties and regions were analysed by inductively coupled plasma mass spectrometry (ICP-MS) for the concentrations of calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sodium (Na), sulphur (S), iron (Fe), boron (B) and manganese (Mn). Samples were scanned in transmittance mode (1mm path length) in a monochromator instrument (400–2500nm). The spectra were pre-treated by second derivative and standard normal variate (SNV) prior to developing calibration models using partial least squares (PLS) regression method with cross-validation. The highest coefficients of determination in cross-validation () and the lowest errors of cross-validation (SECV) were obtained for Ca (0.90 and 9.80mgL−1), Fe (0.86 and 0.65mgL−1) and for K (0.89 and 147.6mgL−1). Intermediate (<0.80) and SECV were obtained for the other minerals analysed. The results showed that some macro- and microelements present in wine might be measured by VIS–NIRS spectroscopy. [Copyright &y& Elsevier]

Abstract: This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths. [Copyright &y& Elsevier]

Abstract: Visible (Vis) and near infrared reflectance (NIR) spectroscopy were used to predict dry matter (DM), acid and neutral detergent fibre (ADFom and aNDFom), ash, crude protein (CP) and pH in wet whole maize (WWM) silage samples. Samples were analysed by reference methods and spectra collected using a NIR spectrophotometer in reflectance (400–2500nm). Predictive equations were developed using modified partial least squares (MPLS) with full cross validation using raw spectra or second derivative. Two scatter corrections were also used, namely standard normal variate and detrend (SNVD) and multiplicative scatter correction (MSC). Coefficient of determination in calibration and standard error of cross validation (SECV) were 0.91 (SECV: 6.5gkg−1 DM) for CP, 0.85 (SECV: 27.4gkg−1) for DM using SNVD, and 0.86 (SECV: 22.1gkg−1 DM) for ADFom using raw spectra. Poor correlations between the reference methods and NIR were found for aNDFom, ash, OMD and pH. It was concluded that NIR spectroscopy might be a suitable method to predict DM, CP and ADFom on WWM silage samples with minimal sample preparation. [Copyright &y& Elsevier]

Abstract: The potential of near-infrared reflectance spectroscopy (NIRS) to predict soil organic C in different particle-size fractions was evaluated. Soil samples (n =180) from various crop rotations in Uruguay were analysed by standard chemical and NIRS methods. Partial least squares (PLS) regression with cross validation was used to develop calibrations between reference data and NIRS spectra (n =87) and validated using an independent set of samples (n =87). Coefficients of determination in calibration () and standard errors in cross validation (SECV) were 0.90 and 0.6 for coarse-sand C, 0.92 and 0.4 for fine-sand C, and 0.96 and 2.1 for clay+silt C, respectively. Calibrations were poor for C/N ratio ( <0.65). Although NIRS demonstrated great potential to predict soil organic C in different particle-size fractions, the nature of sampling and number of samples analysed should be considered in future developments. [Copyright &y& Elsevier]

Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein (CP) content on intact sunflower seeds (Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration (n=250) where the remaining samples (n=50) were used as validation set. Two mathematical treatments (first and second derivative), none (log 1/R) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient of determination in calibration (Rcal2) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96 (SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg-1 on a dry weight basis (second derivative, 400–2500 nm). Prediction models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs. [Copyright &y& Elsevier]

The use of near infrared reflectance spectroscopy (NIRS) was explored to predict trace mineral concentrations in two legumes. Samples (332), composite of white clover (n=97) and lucerne (n=235), from different locations in Uruguay representing a wide range of soil types, were analysed for sodium (Na), sulphur (S), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), and boron (B). The samples were scanned in reflectance in a monochromator instrument (400–2500 nm). Calibration models (n=262) were developed using modified partial least squares regression (MPLS) based on cross-validation and tested using a validation set (n=70). Two mathematical treatments of the spectra were compared (first and second derivative). The highest coefficients of determination in calibration (RCAL2) and the lowest standard errors of cross-validation (SECV) were obtained using second derivative. The RCAL2 and SECV were 0.83 (SECV: 0.8) for Na and 0.86 (SECV: 2.5) for S in g kg−1 DM; 0.80 (SECV: 4.4), 0.80 (SECV: 10.6), 0.78 (SECV: 22.9), 0.76 (SECV: 0.83) and 0.57 (SECV 25.7) for B, Zn, Mn, Cu and Fe in mg kg−1 DM on a dry weight, respectively. Sulphur (SEP: 5.5), sodium (SEP: 1.2) and boron (SEP 4.2) were well predicted by NIRS on a validation set. [Copyright &y& Elsevier]

Abstract: Chlamydia pneumoniae is an intracellular pathogen and an important cause of respiratory tract infections in humans and more recently it has been associated with chronic diseases such as atherosclerosis. Numerous studies have been performed to show the “infectious” hypothesis of atherosclerosis by direct detection of the organisms within atheromatous plaques by seroepidemiological estimation and by animal, immunological and antibiotic interventional studies. In this work we investigated the relation between chronic chlamydial infection, inflammatory markers, Interleukin 7 (IL-7) production and coronary heart disease. We studied 60 patients with coronary heart diseases (CHD), 45 of whom were men and 15 women, with a mean age of 65±5 years, and a control group of 20 healthy subjects, 15 men and 5 women, with a mean age of 60±7 years. Detailed histories including symptoms, risk factors and demographic data were obtained from patients and healthy subjects by administering a standardized questionnaire. Our results demonstrate that the enzyme-linked immunoassay (ELISA) test appears to have a greater sensitivity than the microimmunofluorescence (MIF) technique. 80% of patients had positive IgG to C. pneumoniae and 58% positive IgA to C. pneumoniae with ELISA, while the MIF test showed 68% and 55% positive IgG and IgA to C. pneumoniae, respectively. The control subjects showed 55% positive IgG and 10% IgA to C. pneumoniae by ELISA and 35% positive IgG and 5% IgA to C. pneumoniae by MIF. The combination of positive IgG and IgA to C. pneumoniae was present more frequently than in the control group. Serum levels of IL-7 measured by ELISA were also significantly higher in patients compared to healthy subjects. In conclusion, our study shows that C. pneumoniae IgG and IgA seropositivity, inflammatory markers such as IL-7, fibrinogen, C-reactive protein were significantly correlated with CHD. [Copyright &y& Elsevier]

The objective of this study was to evaluate the feasibility of using near infrared (NIR) spectroscopy combined with principal component analysis (PCA) and partial least squares (PLS) regression to monitor the in vitro hydrolysis of different starch substrates. Potato and rice starches, and pre-gelatinised corn starch were used, where samples collected at different time points (5 to 120 min) during the in vitro hydrolysis and analysed using a Fourier transform NIR instrument with a gold-coated integrating sphere (diffuse reflection). PLS regression models between the spectra and reference data yield a coefficient of determination in cross validation (R2 CV) and standard error in cross validation (SECV) of 0.94 and 1105. 8 μg mL−1; 0.81 and 440.81 μg mL−1; 0.45 and 338 μg mL−1; 0.70 and 276 μg mL−1; 0.75 and 296. 2 μg mL−1 for the prediction of the concentration of maltose using all samples, rice and potato combined, and pre-gelatinised corn, potato and rice starches analysed separately, respectively. It was concluded that the combination of NIR spectroscopy with both PCA and PLS regression might provide with a rapid and efficient tool to rapidly monitor changes that occur during the in vitro hydrolysis of starch. [ABSTRACT FROM AUTHOR]

The increasing desire to control quality in foods in real-time (e.g. on-line, at line, market) demands the development of innovative and easy to use analytical systems [e.g. a combination of sensors with multivariate data analysis (MVA)]. It has been long established that the ability to analyse complex chemical samples such as foods is now essential to achieve the consistent quality demanded by consumers, to ensure uniformity and consistency within a brand and even to avoid fraud, which can have direct implications when it comes to food integrity. It is well known that a broad range of factors (e.g. chemical, physical properties) contribute to the sensory characteristics of foods (e.g. smell, taste and colour) including the origin of raw materials and processing steps. In such complex matrices the use of sensor systems combined with MVA (chemometrics) is especially promising as tools for an encompassing analysis and understanding of these contributing factors. This article reviews recent applications of near infrared sensors (conventional and functional spectroscopy) for the measurement, monitoring and prediction of aroma and taste in several food matrices. • Sensory properties of foods are very important to determine food quality. • Rapid analytical methods based in spectroscopy are currently used by the food industry. • The measurement and prediction of sensory parameters using NIR is discussed. [ABSTRACT FROM AUTHOR]

Purpose To report the biochemical control rate and clinical outcomes with real-time inverse planning (inverse optimization prostate seed implant [IO-PSI]) for favorable-risk (FR) and intermediate-risk (IR) prostate adenocarcinoma in a community practice setting. This analysis is an extended followup of our initial report, with favorable early biochemical control rate (biochemical nonevidence of disease) of 97% at 4 years. Methods and Materials Three hundred fifty-seven evaluable patients with FR and IR prostate cancer underwent real-time IO-PSI (iodine-125/145 Gy or palladium-103/120 Gy) between 2001 and 2013. Results With a median followup of 54 months (range, 24–110 months), the absolute biochemical failure free survival of disease was 96%. The 8-year actuarial probability of prostate-specific antigen failure-free survival for FR and IR cohorts was 92.4% and 87%, respectively. Late genitourinary and gastrointestinal toxicity remained low. Late Grade 2 and Grade 3 genitourinary toxicity was 19% and 1%, respectively. Late Grade 2 and 3 rectal bleeding rates were 1% and 0%, respectively. No difference in biochemical control was observed with preimplant short course androgen deprivation or between Gleason score 3 + 4 vs. 4 + 3 patients. No dosimetric parameter was predictive of biochemical failure. Patients with FR had a significantly decreased risk of failure (hazard ratio = 0.26; 95% confidence interval = 0.09–0.78; p = 0.02) compared with those with IR. Patients with a prostate-specific antigen nadir >0.4 ng/mL had an increased risk of failure (hazard ratio = 1.37; 95% confidence interval = 1.27–1.47; p < 0.0001). Conclusions Our initial biochemical and clinical outcomes using real-time IO-PSI persisted with extended followup and support our original hypothesis for use of a reduced number of sources, needles, and total activity, suggesting that with IO, less is more. [ABSTRACT FROM AUTHOR]

Abstract: The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA. [Copyright &y& Elsevier]

Improving grain yield and adaptation is achieved by synchronising crop phenology with the environment. Phenology research is complex and encounters analytical challenges in characterising genotype x environment (GxE) interactions. This paper presents a simple approach that helps to explain the environmental drivers of phenology. Photoperiod and temperature are major environmental cues for expression of crop developmental genes, and sensitivity to photoperiod is thought to be the major cause of maturity differences among Australian spring barley varieties. However, temperature and photoperiod show similar seasonal trends and strong autocorrelation makes it difficult to distinguish their relative importance in crop development. Partial Least Squares regression (PLS) was developed to handle large data sets with many correlated explanatory variables and only one dependent variable. Across 35 environments encompassing a wide range of temperature and photoperiod regimes in southern Australia, a PLS model described more than 90 % of the phenotypic variation in time to anthesis of three adapted barley cultivars. The PLS outputs defined the critical periods when photoperiod and temperature were most influential, and revealed that temperature effects are of equal or greater importance than photoperiod in determining anthesis date, which is a new finding for genotypes adapted to Australian environments. Insight into the previously elusive differential responses to changes in daily average, maximum, and minimum temperatures will assist in developing flowering models for growers that are more accurate, and assist breeders in the genetic dissection of phenology for target environments. [ABSTRACT FROM AUTHOR]