Simultaneous measurement of two enzyme activities using infrared spectroscopy: A comparative evaluation of PARAFAC, TUCKER and N-PLS modeling.

Enzymes are used in many processes to release fermentable sugars for green production of biofuel, or the refinery of biomass for extraction of functional food ingredients such as pectin or prebiotic oligosaccharides. The complex biomasses may, however, require a multitude of specific enzymes which are active on specific substrates generating a multitude of products. In this paper we use the plant polymer, pectin, to present a method to quantify enzyme activity of two pectolytic enzymes by monitoring their superimposed spectral evolutions simultaneously. The data is analyzed by three chemometric multiway methods, namely PARAFAC, TUCKER3 and N-PLS, to establish simultaneous enzyme activity assays for pectin lyase and pectin methyl esterase. Correlation coefficients Rpred(2) for prediction test sets are 0.48, 0.96 and 0.96 for pectin lyase and 0.70, 0.89 and 0.89 for pectin methyl esterase, respectively. The retrieved models are compared and prediction test sets show that especially TUCKER3 performs well, even in comparison to the supervised regression method N-PLS.

Enzyme activity measurement via spectral evolution profiling and PARAFAC.

The recent advances in multi-way analysis provide new solutions to traditional enzyme activity assessment. In the present study enzyme activity has been determined by monitoring spectral changes of substrates and products in real time. The method relies on measurement of distinct spectral fingerprints of the reaction mixture at specific time points during the course of the whole enzyme catalyzed reaction and employs multi-way analysis to detect the spectral changes. The methodology is demonstrated by spectral evolution profiling of Fourier Transform Infrared (FTIR) spectral fingerprints using parallel factor analysis (PARAFAC) for pectin lyase, glucose oxidase, and a cellulase preparation.

Analysis of pregerminated barley using hyperspectral image analysis.

Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel's lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI(95%): 29-35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI(95%): 0-15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.

Temporal, biochemical and structural factors that influence beef quality measurement using near infrared spectroscopy.

This study included simultaneously measured pre and post-rigor meat quality indicators and attributes, using near infrared (NIR) spectroscopy and reference methods, to understand the temporal, biochemical and structural factors that influence beef quality and use this knowledge to build calibrations for measurement of meat quality using NIR. Eighty beef M. longissimus lumborum (LL) were measured from early pre-rigor (pH, glycogen concentration, and temperature) through to completion of post-rigor ageing (pH, shear force, and water holding capacity (WHC)). To create a range in the attributes, the LLs were subjected to various pre-rigor treatments, including electrical stimulation, restraint wrapping and cooling temperature (5°C, 15°C, or 35°C). Alongside the reference quality measurements and throughout the pre and post-rigor period, the LLs were measured with a diode array NIR spectroscopy system. NIR reflectance measurements were shown to be correlated to reference measurements of pre and post-rigor pH (R(validation)(2)=0.84), pre-rigor glycogen content (R(validation)(2)=0.70), post-rigor shear force (R(validation)(2)=0.58) and post-rigor WHC (R(validation)(2)=0.68). The shape of the NIR and reference plots over time and the ability of NIR to correctly measure differences in treatments indicated that NIR was not merely measuring changes that occur over time, but was measuring specific biochemical and physical changes, most likely changes in glycolytic metabolites, muscle shortening and/or proteolysis. Accounting for the reference method variance showed NIR measurement accuracy to be as good as or slightly better than that of the reference method.