Product quality control is a prerequisite for ensuring safety, effectiveness, and stability. However, because of the different strain species and fermentation processes, there was a significant difference in quality. As a result, they should be clearly distinguished in clinical use. Among them, the fermentation process is critical to achieving consistent product quality. This study aims to introduce near-infrared spectroscopy analysis technology into the production process of fermented Cordyceps powder, including strain culture, strain passage, strain fermentation, strain filtration, strain drying, strain pulverizing, and strain mixing. First, high performance liquid chromatography (HPLC) was used to measure the total nucleosides content in the production process of 30 batches of fermented Cordyceps powder, including uracil, uridine, adenine, guanosine, adenosine, and the process stability and interbatch consistency were analyzed with traditional Chinese medicine (TCM) fingerprinting, followed by the near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) to establish a quantitative analysis model of total nucleosides for online process monitoring of fermented Cordyceps powder preparation products. The model parameters indicate that the established model with good robustness and high measurement precision. It further clarifies that the model can be used for online process monitoring of fermented Cordyceps powder preparation products.
The monitoring of the effluent of a rotary dryer has been developed and implemented. The vapor stream between the dryer and the vacuum is monitored in real time by a process fiber-optic coupled near-infrared (NIR) spectrometer. A partial least-squares (PLS) calibration model was developed on the basis of solvents typically used in a chemical pilot plant and uploaded to an acousto-optic tunable filter NIR (AOTF-NIR). The AOTF-NIR is well suited to process monitoring as it electrically scans a crystal and hence has no moving parts. The AOTF-NIR continuously fits the PLS model to the currently collected spectrum. The returned values can be used to follow the drying process and determine when the material can be unloaded from the dryer. The effluent stream was monitored by placing a gas cell in-line with the vapor stream. The gas cell is fiber-optic coupled to a NIR instrument located 20 m away. The results indicate that the percent vapor in the effluent stream can be monitored in real time and thus be used to determine when the product is free of solvent.
Ground and emulsified samples from Iberian pig hams were analysed by reflectance and interactance reflectance mode. Spectral errors due to intra-sample variations were calculated for both analysis modes. The spectral errors were calculated by means of the STD RMS statistic included on the ISI software. The results obtained show that a mean STD RMS value as low as 4200, could be obtained for paired subsamples of the same sample and that an STD limit of 4374 could be fixed at the instrument set-up program in order to ensure that a representative spectrum has been obtained from two subsamples readings of the same sample. That procedure avoids the need to take numerous subsamples, as is traditional in NIR/NIT meat analysis. The results also show that the spectral repeatability using fiber optic is worse than for spinning cups and it has been concluded that effort should be made to avoid moisture variations during scanning in order to improve spectral repeatability.
In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.
Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process