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A major difficulty in operating biorefinery processes is the large feedstock variability. A systematic multivariate analysis (sMVA) strategy for improved process understanding of industrial biorefinery processes is proposed to support identification of effects of feedstock and process variability on product quality. This sMVA strategy comprises nine steps categorized in data set organization, exploratory analysis, and regression. Different MVA techniques are used, such as principal component analysis (PCA) and partial least squares regression (PLS). As a case study, two main operations in fatty acid production are investigated: oil hydrolysis and fatty acid distillation. Key feedstock properties and process parameters affecting the product properties were identified for both operations. For fatty acid production, product quality largely depends on the type of fat or oil used, such as canola or palm oil, due to the large difference in composition and quality between the oil types. However, if a single oil type is used, the variability in product quality does not always critically depend on the variability in feedstock properties. For both operations, flow rate variations, mainly caused by planning issues, were identified as a main cause. In oil hydrolysis, the feed flow rate influences the residence time and thereby directly influences the hydrolysis and side reactions. In fatty acid distillation, a better control is required of the middle reflux ratio in relation to this changing flow rate. The case study showed that applying our proposed sMVA strategy improves the understanding of a biorefinery process by identifying critical sources of variability, which allows more targeted decisions for optimization and control.

A particle size distribution (PSD) estimation method based on light-scattering properties was validated on experimental visible/near-infrared scattering spectra of polystyrene suspensions, with a nominal particle size ranging from 0.1 to 12 μm in diameter. On the basis of μsand gspectra extracted from double integrating sphere measurements, good PSD estimates were obtained for particles ≥1 μm. The particle volume fraction estimates in the case of μswere close to the target concentrations, although influenced by small baseline fluctuations on the spectra. For submicrometer particles, on the other hand, the non-oscillating μsspectra lack discriminating power, resulting in erroneous PSD estimates. The reduced scattering coefficient spectra (μs′) were found less useful for particle size estimation as they lack a characteristic shape, causing an over- or underestimation of the distribution width. In summary, the estimation routine proved to deliver PSD estimates in line with the reference measurements for micrometer-sized or larger particles based on their μsand gscattering spectra. Additional validation on more polydisperse samples forms the next step before going to bimodal PSD estimates.

The physiological control of stomatal opening by which plants adjust for water availability has been extensively researched. However, the impact of water availability on stomatal development has not received as much attention, especially for amphistomatic plants. Therefore, the acclimation of stomatal development in basil (Ocimum basilicumL.) leaves was investigated. Our results show that leaves developed under water-deficit conditions possess higher stomatal densities and decreased stomatal length for both the adaxial and abaxial leaf sides. Although the stomatal developmental reaction to water deficit was similar for the two leaf surfaces, it was proven that adaxial stomata are more sensitive to water stress than abaxial stomata, with more closed adaxial stomata under water-deficit conditions. Furthermore, plants with leaves containing smaller stomata at higher densities possessed a higher water use efficiency. Our findings highlight the importance of stomatal development as a tool for long-term acclimation to limit water loss, with minimal reduction in biomass production. This highlights the central role that stomata play in both the short (opening) and long-term (development) reaction of plants to water availability, making them key tools for efficient resource use and anticipation of future environmental changes.

Accurate knowledge of the optical properties of turbid media in the light path is important in NIR absorption spectroscopy of biological tissues where multiple scattering complexes the collected light signals due to the non-uniform tissue architecture. Several approaches such as time resolved spectroscopy and spatially resolved spectroscopy have been proposed to measure the bulk optical properties of turbid media. Among them, double integrating sphere (DIS) measurements are recognized as the "golden standard" for in vitro optical properties measurement of turbid media because of its high accuracy and robustness in different conditions. A DIS system is convenient to measure the in vitro optical properties of turbid media like intralipid solutions and biological tissues, since it measures the diffuse reflectance and transmittance simultaneously. However, DIS measurements have been mostly limited to the optical window region (400-1000 nm) or suffered from low signal levels on the detectors due to the absorption by water in the NIR region. In this study, we developed a DIS system for optical property measurement in the 1300-2350 nm region based on a novel wavelength tunable spectroscopic setup which incorporates a high power broadband supercontinuum laser and a high precision monochromator. With this system, optical properties of intralipid solutions were measured in the wavelength region of 1300-2350nm.

Visible and near-infrared spectroscopy have been widely used for non-destructive and rapid monitoring of biological materials including agricultural produce and foods. Methods for decomposition of spectra of biological materials into that of each layer are demanded because the target of monitoring is in most cases limited to a single layer (e.g. sweetness of the flesh of a fruit, not the skin). Also, separation of light absorption and scattering properties is considered to lead more robust modeling of biological materials. Therefore, the potential of Parallel factor analysis (PARAFAC) on spatially resolved spectroscopy (SRS) has been investigated in this study. Spatially resolved spectra of 120 ‘Braeburn’ apples with difference in skin color (green and red) and storage condition (before and after 14 days shelf-life storage) were measured in the 400-1100nm range using 7 detection fibers with different source-detector distance. PARAFAC on the whole data set could decompose the spatially resolved spectra into that of flesh and skin as factor 1 and 2 wavelength loadings, respectively. This result was validated by the facts that the factor 1 loading was almost identical to the ‘real’ spectra of flesh, which had been acquired by measuring peeled apples, and peaks observed in the factor 2 loading corresponded to absorption bands of pigments contained in apple skin. When PARAFAC was applied to individual sample data sets, difference in skin color and chlorophyll content as well as degradation of chlorophyll in skin during ripening and storage could be modelled as the change in factor 1 wavelength loading. These results support the potential of SRS combined with PARAFAC to decompose the spectra of multi-layered biological materials.

Inverse modelling techniques, such as principal component regression, partial least squares regression and support vector machines, are very powerful multivariate calibration strategies which are widely used in near infrared spectroscopy. However, these techniques are so efficient in finding correlations between the spectral variables and the parameter to be predicted that great care should be taken to avoid over-optimistic results by use of a proper validation strategy. In this study, different validation strategies were investigated on a dataset that was acquired during various measurement days. The goal was to predict albumen freshness based on spectral measurements. Validation procedures frequently applied in practice, i.e. 10-fold cross-validation (10-fold CV) and validation based on random subdivision in calibration and validation set (RS) were compared to a cross-validation across measuring day (MD). Whereas 10-fold CV and RS validation suggested that prediction of albumen freshness is possible, MD validation on the same dataset indicated that albumen freshness cannot be predicted from the spectral measurements. It is shown that inverse modelling is very sensitive to unspecific correlations between the spectral measurements and the dependent variable, which might be artifacts of the measurement protocol and will not be persistent in the future. Therefore, selection of the right validation strategy for a given application and critical evaluation of the obtained results are crucial steps in inverse modelling to obtain useful calibration models. More specifically, in the context of process analytical technology where spectra are acquired over time, great care should be taken to break the unspecific correlation between the dependent variable and the variations in the spectral measurements over time.

Inverse modelling techniques, such as principal component regression, partial least squares regression and support vector machines, are very powerful multivariate calibration strategies which are widely used in near infrared spectroscopy. However, these techniques are so efficient in finding correlations between the spectral variables and the parameter to be predicted that great care should be taken to avoid over-optimistic results by use of a proper validation strategy. In this study, different validation strategies were investigated on a dataset that was acquired during various measurement days. The goal was to predict albumen freshness based on spectral measurements. Validation procedures frequently applied in practice, i.e. 10-fold cross-validation (10-fold CV) and validation based on random subdivision in calibration and validation set (RS) were compared to a cross-validation across measuring day (MD). Whereas 10-fold CV and RS validation suggested that prediction of albumen freshness is possible, MD validation on the same dataset indicated that albumen freshness cannot be predicted from the spectral measurements. It is shown that inverse modelling is very sensitive to unspecific correlations between the spectral measurements and the dependent variable, which might be artifacts of the measurement protocol and will not be persistent in the future. Therefore, selection of the right validation strategy for a given application and critical evaluation of the obtained results are crucial steps in inverse modelling to obtain useful calibration models. More specifically, in the context of process analytical technology where spectra are acquired over time, great care should be taken to break the unspecific correlation between the dependent variable and the variations in the spectral measurements over time.

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 350-2200 nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding-doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses.

Food quality is critically determined by its microstructure and composition. These properties could be quantified noninvasively by means of optical properties (absorption and reduced scattering coefficients) of the food samples. In this research, a spatially-resolved spectroscopy setup based on a fiber-optic probe was developed for acquiring spatiallyresolved diffuse reflectance of three sugar foams with different designed microstructures in the range 500 - 1000 nm. A model for light propagation in turbid media based on diffusion approximation for solving the radiative transport equation was employed to derive optical properties (absorption and reduced scattering coefficients) of these foams. The accuracy of this light propagation model was validated on four liquid phantoms with known optical properties. The obtained results indicated that the optical properties estimation was successfully validated on these liquid phantoms. The estimated reduced scattering coefficients s' of the foams clearly showed the effect of foaming time on their microstructures. The acquired absorption coefficients a were also in good agreement with the designed ingredients of these sugar foams. The research results clearly support the potential of spatially-resolved spectroscopy for nondestructive food quality inspection and process monitoring in the food industry.