Search

Modern dairy factories produce thousands of cheese blocks per day. Cheese quality is partly defined by the concentration of dry matter and fat. In this study, we evaluated three different near infrared spectroscopy instruments for on-line determination of fat and dry matter in cheese blocks of approx. size 35 × 28 × 12 cm: scanning reflection (908–1676 nm), scanning interaction (760–1040 nm), and imaging interaction measurements (760–1040 nm). The near infrared measurements were performed on fresh cheese blocks in a pilot plant at three different critical control points (CCP): (CCP1) before pressing, (CCP2) after pressing, and (CCP3) after salting. A total of 160 cheeses from 10 production batches were measured. Whereas near infrared measurements were obtained from the surface of the cheese blocks, the reference analysis was done on a cross-section of the cheese blocks. In general, good results were obtained regressing the reference values onto the near infrared measurements using partial least squares regression. For example, using near infrared scanning reflection at CCP2 yielded root mean squared errors of cross-validation on 0.44% and 0.64% for fat and dry matter, respectively. Hence, surface chemistry of cheese blocks were representative for the average chemistry of the blocks. Furthermore, this study finds that it is possible to predict fat and dry matter at CCP3 based on near infrared measurements obtained at CCP1 earlier in the process. This enables improved control of the cheese making process, as it is possible to detect deviations from target quality early in the production process.

Inhibition of dipeptidyl peptidase IV (DPP-IV) and stimulation of muscle glucose uptake are two of the key strategies for management of type-2-diabetes (T2D). In the present study, four protein hydrolysates generated by enzymatic hydrolysis of chicken by-product, i.e., mechanical chicken deboning residue, were evaluated for their DPP-IV inhibitory activity as well as their effect on glucose uptake by skeletal muscle cells. The DPP-IV inhibitory assay was performed at two concentrations (1000 μg mL−1 and 10 μg mL−1) for the crude chicken protein hydrolysates. The hydrolysate with the highest DPP-IV inhibition was selected for preparative-scale fractionation using size-exclusion chromatography (SEC). The SEC fractions were tested for DPP-IV inhibitory activity as well as their effect on glucose uptake and metabolic activity of skeletal muscle cells. The muscle cells were treated with the SEC fractions and glucose uptake was measured based on luminescence detection of 2-deoxyglucose-6-phosphate (2DG6P). A fraction with peptides in the lower molecular weight range was shown to promote glucose uptake and to inhibit DPP-IV. Further chromatographic fractionation followed by inhibition assaying of the most potent SEC fraction led to isolation of five refined peptide fractions with more than 80% DPP-IV inhibition, which were subsequently analyzed with LC-HRMS/MS. This led to identification of 14 peptides as potential DPP-IV inhibitors from protein hydrolysates of mechanical chicken deboning residue.

In the presented study, Fourier-transform infrared (FTIR) spectroscopy is used to predict the average molecular weight of protein hydrolysates produced from protein-rich by-products from food industry using commercial enzymes. Enzymatic protein hydrolysis is a well-established method for production of protein-rich formulations, recognized for its potential to valorize food-processing by-products. The monitoring of such processes is still a significant challenge as the existing classical analytical methods are not easily applicable to industrial setups. In this study, we are reporting a generic FTIR-based approach for monitoring the average molecular weights of proteins during enzymatic hydrolysis of by-products from the food industry. A total of 885 hydrolysate samples from enzymatic protein hydrolysis reactions of poultry and fish by-products using different enzymes were studied. FTIR spectra acquired from dry-films of the hydrolysates were used to build partial least squares regression (PLSR) models. The most accurate predictions were obtained using a hierarchical PLSR approach involving supervised classification of the FTIR spectra according to raw material quality and enzyme used in the hydrolysis process, and subsequent local regression models tuned to specific enzyme-raw material combinations. The results clearly underline the potential of using FTIR for monitoring protein sizes during enzymatic protein hydrolysis in industrial settings, while also paving the way for measurements of protein sizes in other applications.

According to European food safety authorities, one of the major control parameters for mechanically separated meat is calcium content, which is an indicator of residual bone. Residual bone in mechanically separated meat can also be measured as total ash content. Despite the need to measure both ash and/or calcium content of mechanically separated meat, there is no rapid analytical technique that can be used in an industrial environment. In the current study, we are presenting the first application of Raman spectroscopy as a rapid tool for estimating calcium and ash contents in bone and meat mixtures from mechanical deboning of chicken meat. Raman-based partial least squares regression models were developed for prediction of both ash and calcium content in 79 samples gathered from four different production days. Two different data pre-processing methods, i.e., polynomial background fitting and extended multiplicative scatter correction with polynomial extension, were applied to the raw Raman data and the prediction models were compared. The prediction model based on EMSC treated data afforded the lowest root mean square error of cross-validation (RMSECV = 0.333 g/100 g for calcium and RMSECV = 0.634 g/100 g for ash) and the highest coefficient of determination (R2 = 0.775 for calcium and R2 = 0.894 for ash).

Enzymatic protein hydrolysis (EPH) is one of the industrial bioprocesses used to recover valuable constituents from food processing by-products. Extensive heterogeneity of by-products from, for example, meat processing is a major challenge in production of protein hydrolysates with stable and desirable quality attributes. Therefore, there is a need for process control tools for production of hydrolysates with defined qualities from such heterogeneous raw materials. In the present study, we are reporting a new feed-forward process control strategy for enzymatic protein hydrolysis of poultry by-products. Four different spectroscopic techniques, i.e., NIR imaging scanner, a miniature NIR (microNIR) instrument, fluorescence and Raman, were evaluated as tools for characterization of the raw material composition. Partial least squares (PLS) models for ash, protein, and fat content were developed based on Raman, fluorescence, and microNIR measurements, respectively. In an effort to establish feed-forward process control tools, we developed statistical models that enabled prediction of end-product characteristics, i.e., protein yield and average molecular weight of peptides (Mw), as a function of raw material quality and hydrolysis time. A multiblock sequential orthogonalised-PLS (SO-PLS) model, where spectra from one or more techniques and hydrolysis time were used as predictor variables, was fitted for the feed-forward prediction of product qualities. The best model was obtained for protein yield based on combined use of microNIR and fluorescence (R2 = 0.88 and RMSE = 4.8). A Raman-based model gave a relatively moderate prediction model for Mw (R2 = 0.56 and RMSE = 150). Such statistical models based on spectroscopic measurements of the raw material can be vital process control tools for EPH. To our knowledge, the present work is the first example of a spectroscopic feed-forward process control for an industrially relevant bioprocess.

Forskolin is a unique structurally complex labdane-type diterpenoid used in the treatment of glaucoma and heart failure based on its activity as a cyclic AMP booster. Commercial production of forskolin relies exclusively on extraction from its only known natural source, the plant Coleus forskohlii, in which forskolin accumulates in the root cork. Here, we report the discovery of five cytochrome P450s and two acetyltransferases which catalyze a cascade of reactions converting the forskolin precursor 13R-manoyl oxide into forskolin and a diverse array of additional labdane-type diterpenoids. A minimal set of three P450s in combination with a single acetyl transferase was identified that catalyzes the conversion of 13R-manoyl oxide into forskolin as demonstrated by transient expression in Nicotiana benthamiana. The entire pathway for forskolin production from glucose encompassing expression of nine genes was stably integrated into Saccharomyces cerevisiae and afforded forskolin titers of 40 mg/L. DOI: http://dx.doi.org/10.7554/eLife.23001.001, eLife digest Unlike animals, plants cannot move away from a herbivore or other threats. Instead, they have evolved to produce a vast array of chemical compounds to protect themselves. Some of these compounds are also important to humans, for example, as medicines or fragrances. Plants usually only produce small amounts of these compounds in mixtures with many other compounds, which makes it difficult to purify them. As a result, the methods of purifying the compounds may require huge amounts of plant material, or be expensive and not environmentally friendly. One solution to this would be to genetically engineer microbes like bacteria or yeast to produce the compounds instead. In order to do that, we need to understand exactly which enzymes the plant uses to make each compound and introduce them into suitable microbes. A compound called forskolin has been used since ancient times in traditional Indian medicine to treat conditions like high blood pressure, asthma and heart complications. Forskolin is found exclusively in the root of a plant called Coleus forskohlii, which is native to India and south-east Asia. It is stored inside cells within the bark of the root in structures called oil bodies, which are similar to oil drops. However, it is not known where forskolin is made, or which enzymes are involved. Pateraki, Andersen-Ranberg et al. set out to uncover how C. forskohlii produces this compound. The experiments show that forskolin is produced within the cells that contain the oil bodies. A technique called RNA sequencing was used to identify several genes that are highly active in these cells and encode enzymes that could potentially be involved in producing forskolin. Further experiments demonstrated that these enzymes drive a cascade of chemical reactions that convert a molecule called 13R-manoyl oxide into forskolin. Next, Pateraki, Andersen-Ranberg et al. inserted the genes into yeast cells that could already produce 13R-manoyl oxide, which allowed the yeast to produce relatively high amounts of forskolin. These findings show that it is possible to identify the genes involved in the production of medicinal compounds in a relatively short amount of time. This knowledge will aid the development of a method that can be used to produce forskolin and other similar compounds on a large scale without needing to harvest C. forskohlii plants. DOI: http://dx.doi.org/10.7554/eLife.23001.002