Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs

Background： The ability of near-infrared reflectance spectroscopy（NIRS） to determine the digestible energy（DE）and metabolizable energy（ME） content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set（n = 88） and a validation set（n = 29）. The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez（J Anim Sci. 71：3389–98,1993）. The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square（MPLS） regression method based on77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.Results： The NIRS equations obtained from actually determined DE, the coefficient of determination for calibration（RSQcal）, cross-validation（R^2CV）, and validation（RSQv） were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R^2CV, and RSQvvalues were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME（RPD_v= 2.38, 〈 2.50）, the other three equations built from actually determined energy and calculated DE produced good prediction performance（RPD_vranging from 2.53 to 2.69, 〉 2.50） when applied to validation samples.Conclusion： These results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.

Geographic Classification of Chinese Grape Wines by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Fructus cnidii （Chinese name shechuangzi） is the fruit produced by Cnidium monnieri （L.） Cusson （Umbelliferae）. It is a perennial herb that is used to treat skin-related diseases and gynecopathyell. Recent pharmacological studies have revealed crude extracts or components isolated from fructus cnidii possess antiallergic, antipruritic, antidermatophytic, antibacterial, antifungal, and antiosteoporotic activities. Osthole and imperatorin are the major compounds present in shechuangzi. They are often used as standards for the evaluation of the quality of shechuangzi products.

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (Elymus glabriflorus) Using Near-Infrared Reflectance Spectroscopy

Southeastern wildrye (Elymus glabriflorus, Vasey ex L.H. Dewey) is a cool-season, perennial grass native to southeastern United States. Recently, there is a growing interest in its development as a grazing and haying forage crop due to its wide area of adaptation across this region. Consequently, there is a great need for the evaluation of its forage quality by rapid, but accurate analytical methods like Near-Infrared Reflectance Spectroscopy (NIRS). In this study, acceptable NIRS calibration models were developed for: dry matter, DM (n = 113, R2 = 0.904, RSCD = 2.54, RSCIQ = 4.65);crude protein, CP (n = 113, R2 = 0.974, RSCD = 5.16, RSCIQ = 5.92);acid detergent fiber, ADF (n = 116, R2 = 0.896, RSCD = 2.35, RSCIQ = 1.28);neutral detergent fiber, NDF (n = 118, R2 = 0.934, RSCD = 2.53, RSCIQ = 3.38);digestible dry matter, DDM (n = 116, R2 = 0.895, RSCD = 2.36, RSCIQ = 1.35);dry matter intake, DMI (n = 115, R2 = 0.924, RSCD = 2.40, RSCIQ = 2.53);and relative feed value, RFV (n = 114, R2 = 0.932, RSCD = 2.94, RSCIQ = 2.81). Prediction of independent validation sets yielded good agreement between the NIRS predicted values and the laboratory reference values for each of: DM (n = 53, R2 = 0.831, RPD = 2.45, RPIQ = 4.24);CP (n = 57, R2 = 0.967, RPD = 5.37, RPIQ = 7.16);ADF (n = 49, R2 = 0.895, RPD = 2.97, RPIQ = 1.51);NDF (n = 53, R2 = 0.928, RPD = 3.75, RPIQ = 4.22);digestible dry matter, DDM (n = 55, R2 = 0.860, RSCD = 265, RSCIQ = 1.15);dry matter intake, DMI (n = 156, R2 = 0.845, RSCD = 2.48, RSCIQ = 2.11);and relative feed value, RFV (n = 55, R2 = 0.916, RSCD = 3.45, RSCIQ = 3.04) contents, indicating that all seven calibration models had good quantitative information. Therefore, precise, accurate, and rapid analysis of these important forage quality attributes of southeastern wildrye can be routinely done using the developed NIRS calibration models.

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

Cortical activity in patients with high-functioning ischemic stroke during the Purdue Pegboard Test:insights into bimanual coordinated fine motor skills with functional near-infrared spectroscopy

After stroke,even high-functioning individuals may experience compromised bimanual coordination and fine motor dexterity,leading to reduced functional independence.Bilateral arm training has been proposed as a promising intervention to address these deficits.However,the neural basis of the impairment of functional fine motor skills and their relationship to bimanual coordination performance in stroke patients remains unclear,limiting the development of more targeted interventions.To address this gap,our study employed functional near-infrared spectroscopy to investigate cortical responses in patients after stroke as they perform functional tasks that engage fine motor control and coordination.Twenty-four high-functioning patients with ischemic stroke(7 women,17 men;mean age 64.75±10.84 years)participated in this cross-sectional observational study and completed four subtasks from the Purdue Pegboard Test,which measures unimanual and bimanual finger and hand dexterity.We found significant bilateral activation of the sensorimotor cortices during all Purdue Pegboard Test subtasks,with bimanual tasks inducing higher cortical activation than the assembly subtask.Importantly,patients with better bimanual coordination exhibited lower cortical activation during the other three Purdue Pegboard Test subtasks.Notably,the observed neural response patterns varied depending on the specific subtask.In the unaffected hand task,the differences were primarily observed in the ipsilesional hemisphere.In contrast,the bilateral sensorimotor cortices and the contralesional hemisphere played a more prominent role in the bimanual task and assembly task,respectively.While significant correlations were found between cortical activation and unimanual tasks,no significant correlations were observed with bimanual tasks.This study provides insights into the neural basis of bimanual coordination and fine motor skills in high-functioning patients after stroke,highlighting task-dependent neural responses.The findings also suggest that patients who exhibit better bimanual performance demonstrate more efficient cortical activation.Therefore,incorporating bilateral arm training in post-stroke rehabilitation is important for better outcomes.The combination of functional near-infrared spectroscopy with functional motor paradigms is valuable for assessing skills and developing targeted interventions in stroke rehabilitation.

Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration

Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality seedlings.Nearinfrared spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor N.Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index(VSI).Here,we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emargiaata and developed a new VSI for this task.Plants were grown in a hydroponics system with 0,2.75,5.5or 11 mM N for 45 days.Then we measured gas exchange,chlorophylla fluorescence,and pigments in leaves;analyzed complete leaf nutrients,and recorded spectral data for leaves at 966 to 1685 nm using NIRS.With a statistical learning approach,the dimensionality of the spectral data was reduced,then models were generated using two classes(N deficiency,N)or four classes(0,2.75,5.5,11 mM N).The best combination of techniques for dimensionality reduction and classification,respectively,was stepwise regression(PROC STEPDISC)and linear discriminant function.It was possible to detect N deficiency in seedlings leaves with 100%precision,and the four N concentrations with93.55%accuracy before photosynthetic damage to the plant occurred.Thereby,NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A.emarginata.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

RAPID DETERMINATION OF PROTEIN IN MILLET BY FOURIER TRANSFORM NEAR-INFRARED(FTNIR)DIFFUSE REFLECTANCE SPECTROSCOPY

In this paper,the Fourier transform near-infrared(FTNIR)diffuse reflectance spectroscopy is applied for the rapid determination of protein in millet.The partial least-squares(PLS)regression is successfully used as an effective multivariate calibration technique.The calibration set is composed of 20 standard millet samples that the protein contents were determined by the traditional Kjeldahl method.The optimal model dimension is found to be 5 by cross-validation.22 millet samples were determined by the proposed FTNIR-PLS method.The correlation coefficient between the concentration values obtained by the FTNIR-PLS method and the traditional Kjeldahl method is 0.9805.The standard error of prediction(SEP)is 0.28% and the mean recovery is 100.2%.The proposed method has been successfully applied for the routine analysis of protein in about 10,000 grain samples.

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Interactions between the central nervous system(CNS)and autonomic nervous system(ANS)play a crucial role in modulating perception,cognition,and emotion production.Previous studies on CNS–ANS interactions,or heart–brain coupling,have often used heart rate variability(HRV)metrics derived from electrocardiography(ECG)recordings as empirical measurements of sympathetic and parasympathetic activities.Functional near-infrared spectroscopy(fNIRS)is a functional brain imaging modality that is increasingly used in brain and cognition studies.The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms.Therefore,fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously,allowing analysis of CNS–ANS interactions.In this proof-of-concept study,fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena(MOBA)game.From the fNIRS recordings,neural and heartbeat frequency bands were extracted to assess prefrontal activities and shortterm pulse rate variability(PRV),an approximation for short-term HRV,respectively.Under the experimental conditions used,fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards,in terms of absolute measurements and video game playing(VGP)-related changes.It was also observed that,similar to previous studies on physical activity and exercise,the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP.It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP,facilitating the understanding of VGP-related heart–brain coupling.

Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

Near-infrared spectroscopy as a promising tool in stroke:Current applications and future perspectives

Stroke is caused by an acute focal disruption of the vasculature in the central nervous system.Neurological-related functional deficits are the most devastating consequences for stroke survi-vors.Neural signals from stroke patients can reflect the functional statuses of patients and provide insights into the neuronal recovery mechanism for functioning,which could be used as the basis for designing optimal treatment strategies.Near-infrared spectroscopy(NIRS)is a low-cost,noninvasive,easily operated neuroimage method and it is compatible with various rehabilitative programs.These advantages make NIRS an excellent candidate in research for stroke recovery.Here,we focused on the brain functions and recovery for stroke patients at stable status,conducted a systematic literature review about NIRS applications in stroke since 2000 and identified a total of 72 references through ScienceDirect and PubMed database retrieval.The NIRS studies in stroke include resting-state function and its recovery,motor function and itsrecovery,motor and cognition interference,cognitive function and its recovery,language function and its recovery,emotional function and its recovery and other applications.Based on the results of the quality assessment,we identified some study gaps from the previous research and provided suggestions for some methodological improvement in the future.The trend of NIRS gives a boost to its application in stroke,and the potential research directions for NIRS in stroke are pros-pected,including multi-center clinical research,treatment efficacy prediction research and brain-muscle coupling research.Finally,limitations of NIRS are discussed.

Identification of Salmonella and Listeria monocytogenes by Near-infrared Spectroscopy and PCA

Near-infrared spectra of pathogenic bacteria (salmonella and Listeria monocytogenes ) were determined, and the spectral data were analyzed by the projection discriminant analysis based on principal component analysis (PCA). The expected results were obtained. The results showed that salmonella and L. monocytogenes could be distinguished from each other by the near-infrared spectroscopy of the whole cells, cell walls or cytoplasm.

Assessment of cerebral oxygenation response to hemodialysis using near-infrared spectroscopy (NIRS):Challenges and solutions

To date,the clinical use of functional near-infrared spectroscopy(NIRS)to detect cerebral ischemia has been largely limited to surgical settings,where motion artifacts are minimal.In this study,we present novel techniques to address the challenges of using NIRS to monitor ambu-latory patients with kidney disease during approximately eight hours of hemodialysis(HD)treatment.People with end-stage kidney disease who require HD are at higher risk for cognitive impairment and dementia than age-matched controls.Recent studies have suggested that HD-related declines in cerebral blood flow might explain some of the adverse outcomes of HD treatment.However,there are currently no established paradigms for monitoring cerebral per-fusion in real-time during HD treatment.In this study,we used NIRS to assess cerebral hemo-dynamic responses among 95 prevalent HD patients during two consecutive HD treatments.We observed substantial signal attenuation in our predominantly Black patient cohort that required probe modifications.We also observed consistent motion artifacts that we addressed by devel-oping a novel NIRS methodology,called the HD cerebral oxygen demand algorithm(HD-CODA),to identify episodes when cerebral oxygen demand might be outpacing supply during HD treatment.We then examined the association between a summary measure of time spent in cerebral deoxygenation,derived using the HD-CODA,and hemodynamic and treatment-related variables.We found that this summary measure was associated with intradialytic mean arterial pressure,heart rate,and volume removal.Future studies should use the HD-CODA to implement studies of real-time NIRS monitoring for incident dialysis patients,over longer time frames,and in other dialysis modalities.

Rapid and simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy and chemometrics

Coptidis Rhizoma(Chinese:Huanglian)and Phellodendri Chinensis Cortex(Chinese:Huangbo)are widely used Traditional Chinese Medicine,and often used in combination because of their similar pharmacological effects in clinical practice.However,the quality control methods of the two drugs are different and complicated,which is time consuming and laborious in practical application.In this paper,rapid and simultaneous determination of moisture and berberine in Coptidis Rhizoma(CR)and Phellodendri Chinensis Cortex(PC)was realized by near-infrared spectroscopy(NIRs)combined with global models.Competitive adaptive reweighted sampling(CARS)and successive projection algorithm(SPA)method were applied for variable selection.Principal component analysis(PCA)and partial least squares regression method(PLSR)were applied for qualitative and quantitative analysis,respectively.The characteristic variables of berberine showed similarity and consistency in distribution,providing basis for the global models.For moisture content,the global model had relative standard error of prediction set(RSEP)value of 3.04%and 2.53%for CR and PC,respectively.For berberine content,the global model had RSEP value of 5.41%and 3.97%for CR and PC,respectively.These results indicated the global models based on CARS-PLS method achieved satisfactory prediction for moisture and berberine content,improving the determination e±ciency.Furthermore,the greater range and larger number of samples enhanced the reliance of the global model.The NIRs combined with global models could be a powerful tool for quality control of CR and PC.

Determination of the immunoglobulin G precipitation end-point by an intelligent near-infrared spectroscopy system

Precipitation is a key manufacturing unit during the immunoglobulin G(IgG)production,which guarantees the quality of the final product.Ethanol is usually used to purify IgG during the precipitation process,so it is important to monitor the ethanol concentration online.Near-infrared(NIR)spectroscopy is a powerful process analytical technology(PAT)which has been proved to be feasible to determine the ethanol concentration during the precipitation process.However,the NIR model is usually established based on the specific process,so a universal model is needed.And the clarity degree of solution will affect the quality of the spectra.Therefore,in this study an integrated NIR system was introduced to establish a universal NIR model which could predict the ethanol concentration online and determine the end-point of the whole process.First,a spectra acquisition device was designed and established in order to get high-quality NIR spectra.Then,a simple prepared ethanol NIR model was constructed to predict the actual manufacturing process.Finally,the end-point was determined to stop the peristaltic pump when the ethanol concentration reached 20%.The results showed that the spectra quality was good,model prediction was accurate,and process monitoring was accurate.In conclusion,all results indicated that the integrated NIR system could be used to monitor the biopharmaceutical process to help us understand the pharmaceutical process.

Feasibility study on the use of near-infrared spectroscopy for rapid and nondestructive determination of gossypol content in intact cottonseeds

Background:Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products.It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program,so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method.Results:Gossypol content in cottonseeds was investigated by near-infrared spectroscopy(NIRS)and high-performance liquid chromatography(HPLC).Partial least squares regression,combined with spectral pretreatment methods including Savitzky-Golay smoothing,standard normal variate,multiplicative scatter correction,and first derivate were tested for optimizing the calibration models.NIRS technique was efficient in predicting gossypol content in intact cottonseeds,as revealed by the root-mean-square error of cross-validation(RMSECV),root-mean-square error of prediction(RMSEP),coefficient for determination of prediction(R_(p)^(2)),and residual predictive deviation(RPD)values for all models,being 0.05∼0.07,0.04∼0.06,0.82∼0.92,and 2.3∼3.4,respectively.The optimized model pretreated by Savitzky-Golay smoothing+standard normal variate+first derivate resulted in a good determination of gossypol content in intact cottonseeds.Conclusions:Near-infrared spectroscopy coupled with different spectral pretreatments and partial least squares(PLS)regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds,rapidly and non destructively.It could be used as an alternative method to substitute for traditional one to determi ne the gossypol content in intact cottonseeds.