Relationship between prescribed opioids, pain management satisfaction, and pain intensity in oncology outpatients.

BACKGROUND: Pain is the most severe and commonest symptom for patients with cancer. Patients' pain management satisfaction is an essential indicator of quality care and further affects their willingness to seek care. PURPOSE: This study aimed to examine the correlations between patients' prescribed opioids, pain management satisfaction, and pain intensity. METHODS: This study adopted a cross-sectional correlation design, recruited a total of 123 patients with cancer pain through convenience sampling, and used two research scales, namely the Chinese version of the Pain Treatment Satisfaction Scale and the Brief Pain Inventory-Short Form. RESULTS: The findings indicated that the correlations of prescribed opioid dosage with pain management satisfaction (r = - .10, p > .05) and pain intensity (worst pain, least pain, average pain, and pain right now; r = - .05 to .01, p > .05) were nonsignificant. The correlations of pain management satisfaction with pain intensity (r = .24 to .32, p < .01), pain interference (r = .32, p < .01), and pain relief (r = - .25, p < .01) were all significant, but that with the worst pain (r = .06, p > .05) was nonsignificant. CONCLUSIONS: Medical professionals providing cancer pain management should focus on medicines strategies and individuals' pain relief requirements. In particular, patients with the worst pain require extra investigations into their needs, and their satisfaction with their level of pain should be further evaluated.

The influence of opioid-taking self-efficacy and social support on pain management satisfaction in outpatients with cancer pain.

PURPOSE: The purpose of this study was to explore the correlations between patients' opioid-taking self-efficacy, social support, and their pain management satisfaction, and to evaluate the effect of social support and opioid-taking self-efficacy in explaining the variance in pain management satisfaction. METHODS: We used a cross-sectional and correlational research design and recruited 123 cancer patients via convenience sampling. We used the following instruments: the Opioid-Taking Self-Efficacy Scale, the Inventory of Socially Supportive Behavior, and the Chinese version of the Pain Treatment Satisfaction Scale. RESULTS: There were significant and negative correlations between opioid-taking self-efficacy and pain management satisfaction (r = - .43, p < .001) and between social support and pain management satisfaction (r = - .47, p < .001). Using a hierarchical regression analysis, social support and opioid-taking self-efficacy explained 17.20% and 5.20%, respectively, of the variance in pain management satisfaction. CONCLUSIONS: The results of this study confirm the importance of social support and opioid-taking self-efficacy in influencing pain management satisfaction. We recommend that professional care providers develop relevant intervention aimed at improving patients' pain management satisfaction.

[The Study on Combined Screening Method for TBS and Cervical Cancer Cell's DNA Quantitative Analysis Based on Multi-Spectral Imaging Technology].

The traditional screening method of cervical cancer mainly involves TBS classification method and quantitative analysis method based on DNA, TBS screening method which has high diagnostic rate, but it needs experienced doctors to participate in the process with low sensitivity. Therefore, it is difficult to achieve early cervical cancer screening; cell's DNA quantitative analysis, only stained the nucleus, achieving a quantitative and automated analysis. Even with high sensitivity, the specificity is poor. So it is extremely necessary to realize the combination of screening for TBS and cell's DNA quantitative analysis, but the current TBS and DNA quantitative analysis combined screening method for the use of two different cell smear, time-consuming, laborious and very inconvenient, there is no screening method for cervical cancer on a combination of TBS and DNA quantitative analysis at home and abroad. This paper presents a method using TBS classification and DNA quantitative analysis on the same cell smear which was stained with Pap and Feulgen in order to solve the problem of the interference of the absorbance of DNA substance caused by multiple staining. a set of multi spectral imaging system and DNA absorbance peeling model are established based on linear multiple regression. With model solution, the real absorbance of the substance DNA was calculated, and the quantitative analysis of DNA was carried out. A pseudo color image is synthesized from 3 - band cell images with the close wavelength of RGB for TBS classification, so the organic combination of TBS and cell's DNA quantitative analysis is realized. Experiments show that the DNA quantitative analysis model of this method is stable, with small error and high diagnostic rate due to the fact that the pseudo color images used for TBS screening were bright, clear, and clear cytoplasm. Therefore, this method is very useful in the diagnosis and screening of cervical cancer.

[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].

To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.

Borneol-Modified Schisandrin B Micelles Cross the Blood-Brain Barrier To Treat Alzheimer's Disease in Aged Mice.

Objective: Schisandrin B (Sch B) is a bioactive dibenzocyclooctadiene derizative that is prevalent in the fruit of Schisandra chinensis. Numerous studies have demonstrated that Sch B has a neuroprotective action by reducing oxidative stress and effectively preventing inflammation. It follows that Sch B is a potential treatment for Alzheimer's disease (AD). However, the drug's solubility, bioavailability, and lower permeability of the blood-brain barrier (BBB) can all reduce its efficacy during the therapy process. Therefore, this study constructed borneol-modified schisandrin B micelles (Bor-Sch B-Ms), which increase brain targeting by accurately delivering medications to the brain, effectively improving bioavailability. High therapeutic efficacy has been achieved at the pathological site. Methods: Bor-Sch B-Ms were prepared using the thin film dispersion approach in this article. On the one hand, to observe the targeting effect of borneol, we constructed a blood-brain barrier (BBB) model in vitro and studied the ability of micelles to cross the BBB. On the other hand, the distribution of micelle drugs and their related pharmacological effects on neuroinflammation, oxidative stress, and neuronal damage were studied through in vivo administration in mice. Results: In vitro studies have demonstrated that the drug uptake of bEnd.3 cells was increased by the borneol alteration on the surface of the nano micelles, implying that Bor-Sch B-Ms can promote the therapeutic effect of N2a cells. This could result in more medicines entering the BBB. In addition, in vivo studies revealed that the distribution and circulation time of medications in the brain tissue were significantly higher than those in other groups, making it more suitable for the treatment of central nervous system diseases. Conclusion: As a novel nanodrug delivery system, borneol modified schisandrin B micelles have promising research prospects in the treatment of Alzheimer's disease.

[Temperature compensation for portable Vis/NIR spectrometer measurement of apple fruit soluble solids contents].

Visible (Vis)/near infrared (NIR) spectroscopy has been used successfully to measure soluble solids content (SSC) in fruit. However, for practical implementation, the NIR technique needs to be able to compensate for fruit temperature fluctuations, as it was observed that the sample temperature affects the NIR spectrum. A portable Vis/NIR spectrometer was used to collect diffused transmittance spectra of apples at different temperatures (0-30 degrees C). The spectral data of apple at 20 degrees C was used to develop a norm partial least squares (PLS) model. Slope/bias technique was found to well suits to control the accuracy of the calibration model for SSC concerning temperature fluctuations. The correctional PLS models were used to predict the SSC of apple at 0, 10 and 30 degrees C, respectively. The correctional method was found to perform well with Q values of 0.810, 0.822 and 0.802, respectively. When no precautions are taken, the Q value on the SSC may be as small as 0.525-0.680. The results obtained highlight the potential of portable Vis/NIR instruments for assessing internal quality of fruits on site under varying weather conditions.

[Determination of fat, protein and DM in raw milk by portable short-wave near infrared spectrometer].

Near infrared diffuse reflectance spectroscopy calibrations of fat, protein and DM in raw milk were studied with partial least-squares (PLS) regression using portable short-wave near infrared spectrometer. The results indicated that good calibrations of fat and DM were found, the correlation coefficients were all 0.98, the RMSEC were 0.187 and 0.217, RMSEP were 0.187 and 0.296, the RPDs were 5.02 and 3.20 respectively; the calibration of protein needed to be improved but can be used for practice, the correlation coefficient was 0.95, RMSEC was 0.105, RMSEP was 0.120, and RPD was 2.60. Furthermore, the measuring accuracy was improved by analyzing the correction relation of fat and DM in raw milk This study will probably provide a new on-site method for nondestructive and rapid measurement of milk.

Purification of Colon Carcinoma Cells from Primary Colon Tumor Using a Filtration Method via Porous Polymeric Filters.

Cancer stem cells (CSCs) or cancer-initiating cells (CICs) are key factors for tumor generation and metastasis. We investigated a filtration method to enhance CSCs (CICs) from colon carcinoma HT-29 cells and primary colon carcinoma cells derived from patient colon tumors using poly(lactide-co-glycolic acid)/silk screen (PLGA/SK) filters. The colon carcinoma cell solutions were permeated via porous filters to obtain a permeation solution. Then, the cell cultivation media were permeated via the filters to obtain the recovered solution, where the colon carcinoma cells that adhered to the filters were washed off into the recovered solution. Subsequently, the filters were incubated in the culture media to obtain the migrated cells via the filters. Colon carcinoma HT-29 cells with high tumorigenicity, which might be CSCs (CICs), were enhanced in the cells in the recovered solution and in the migrated cells based on the CSC (CIC) marker expression, colony-forming unit assay, and carcinoembryonic antigen (CEA) production. Although primary colon carcinoma cells isolated from colon tumor tissues contained fibroblast-like cells, the primary colon carcinoma cells were purified from fibroblast-like cells by filtration through PLGA/SK filters, indicating that the filtration method is effective in purifying primary colon carcinoma cells.

[Study on variable selection of NIR spectral information based on GA and SCMWPLS].

Spectral data compression and informative variable selection are the research focus on the application of NIR, which enable to simplify the model and improve the accuracy of prediction. The research used the pretreatment methods such as the second derivative, normalization and orthogonal signal correction (OSC) to filter irrelevant array according to the concentration of soluble solid content (SSC) based on the Vis/NIR spectroscopy of apricot. SCMWPLS was used to select 880, 894-910 and 932 nm as the regions for constructing prediction PLS model with correlation coefficient (R) of 0.920, standard error of calibration (SEC) of 0.454 and standard error of prediction (SEP) of 0.470 for SSC. Besides, after conducting an independent run for 100 times, GA obtained the regression variables as 888 and 900 nm according to the higher frequency of selection to set up GA. MLR prediction model, and the R, SEC and SEP were 0.905, 0.488 and 0.459 respectively. The results of the two modeling methods are both better than those of full-region PLS model. This demonstrates that OSC enables to filter irrelevant signal array according to the concentration of SSC and reduce the latent variables used for modeling. Also, SCMWPLS and GA can identify the optimal combination of information variables. These methods have a universal significance on building NIR express analysis model with low dimension and high precision.

[Research on fast discrimination between Panax ginseng and Panax quinque folium based on near infrared spectroscopy].

Near infrared spectroscopy combined with pattern recognition techniques were applied to develop a method of fast and nondestructive discrimination between Chinese ginseng and American ginseng. A total of 90 representative ginseng samples including root, fiber and powder were collected. NIR spectra of the samples were obtained directly with wrapped polyethylene packing film. MSC and first derivative were performed after the elimination of notable packing film absorbance in raw spectra. Then the informative wave bands were chosen by moving window partial least-squares regression method. PLS-DA, PCA-DA and SVM discrimination models were founded and their results were compared. SVM was proven to be the most effective method with 100% accurate identification rate for validation set. It indicates that the method founded is precise and convenient and can be practically used in practice for quality control and fast screening of raw herb materials.

Experimental study of thermophysical properties of coal gangue at initial stage of spontaneous combustion.

The specific heat capacity (Cp), thermal conductivity (λ), and thermal diffusion coefficient (D) of coal gangue (CG) are the main factors that affect the self-ignition potential for heat transferring from burning center to the ground surface. In this paper, the thermophysical properties of CG were investigated by transient plane source method. The correlations and sensitivity analysis were performed to characterize the degree of influence of the thermophysical parameters (Cp, λ, and D) dependence on temperature. The mean values of Cp, λ, and D for CG were at a range of 0.73-0.89 J g-1 K-1, 0.44-0.76 W m-1 K-1, and 0.26-0.43 mm2 s-1, respectively. Compared with coal, CG were located in the low area (Sc < 2) with higher value of λ and D, but lower value of Cp. Results also showed that 70 °C was a critical point for CG at which some kinds of mutation took place in thermophysical properties. The comparison between the experimental data and the correlation outputs exhibited consistency.

Enrichment of cancer-initiating cells from colon cancer cells through porous polymeric membranes by a membrane filtration method.

Cancer-initiating cells (CICs) or cancer stem cells (CSCs) are primarily responsible for tumor initiation, growth, and metastasis and represent a few percent of the total tumor cell population. We designed a membrane filtration protocol to enrich CICs (CSCs) from the LoVo colon cancer cell line via nylon mesh filter membranes with 11 and 20 µm pore sizes and poly(lactide-co-glycolic acid)/silk screen (PLGA/silk screen) porous membranes (pore sizes of 20-30 µm). The colon cancer cell solution was filtered through the membranes to obtain a permeate solution. Subsequently, the cell culture medium was filtered through the membranes to collect the recovery solution where the cells attached to the membranes were rinsed off into the recovery solution. Then, the membranes were cultivated in the cultivation medium to collect the migrated cells from the membranes. The cells migrated from any membrane had higher expression of the CSC surface markers CD44 and CD133, had higher colony formation levels, and produced more carcinoembryonic antigen (CEA) than the colon cancer cells cultivated on conventional tissue culture plates (control). We established a method to enrich the CICs (CSCs) of colon cancer cells from migrated cells through porous polymeric membranes by the membrane filtration protocol developed in this study.

[Improving the prediction model of protein in milk powder using GA-PLS combined with PC-ANN arithmetic].

The present paper presents a new NIR analysis method with partial least square regression (PLS) and artificial neural network (ANN) to improve the prediction precision of the protein model for milk powder. First, an efficient method named region selecting by genetic algorithms (RS-GA) was used to select the calibration region, and then the GA-PLS model was made to predict the linear part of the protein content in milk powder. And then in the region selected by RS-GA method, principal component analysis (PCA) was calculated. The principal components were taken as the input of ANN model. The remnant values by subtracting the standard values and the GA-PLS validation values were regarded as the output of ANN. The ANN model was made to predict the nonlinear part of the protein content. The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model. A full region PLS model (Fr-PLS) was also made, and the RMSEP of the Fr-PLS, GA-PLS and GA-PLS+PC-ANN model was 0.511, 0.440 and 0.235, respectively. The results show that the prediction precision of the protein model for milk powder was largely improved when adding the nonlinear port in the NIR model, and this method can also be used for other complex material to improve the prediction precision.

[Selection of variables for MLR in Vis/NIR spectroscopy based on BiPLS combined with GA].

The feasibility of using efficient selection of variables in Vis/NIR for a rapid and conclusive determination of fruit inner qualities such as soluble solids content (SSC) of plums was investigated. A new strategy was proposed in the present paper, i. e. two-stage variable selection using the backward interval partial least squares (BiPLS) combined with genetic algorithm (GA). Firstly, it splits the whole spectral region into equidistant sub-regions and then develops all BiPLS regression models, and the informative regions which are used to constructed PLS models with the lowest error can be located. Secondly, GA method is used to select variable in these informative regions, which are used for regression variables of MLR model. The Vis/NIR spectra containing 225 individual data points were processed by Savizky-Golay filter smoothing and second-order derivative, and 9 sub-regions were selected by BiPLS procedure when the spectra were divided into 25 sub-regions. The optimal 12 variables, which were the output of the GA procedure, were selected by the higher occurrence frequency while the GA procedure ran 100 times. In order to simplify the multiple linear regression (MLR) modeling, the wavelength variables with the maximum occurrence frequency were chosen when the adjacent wavelengths were selected by GA. Finally, 638, 734, 752, 868, 910, 916 and 938 nm were used to build a MLR model. The results show that MLR model produced by BiPLS-GA performs well with correlation coefficients (R) of 0.984, root mean standard error of calibration (RMSEC) of 0.364 and root mean standard error of prediction (RMSEP) of 0.471 for SSC, which outperforms models using stepwise regression analysis (SRA). This work proved that the BiPLS-GA could determine optimal variables in Vis/NIR spectra and improve the accuracy of model.

[Nondestructive measurement of SSC in western pear using genetic algorithms and FT-NIR spectroscopy].

An improved genetic algorithm was used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression (PLS). The region selecting by genetic algorithms (R-SGA) was applied in building calibration model of soluble solid content (SSC) of Western pear, and the numbers of latent variables used to build calibration model were further reduced. The Fourier transform near infrared reflectance (FT-NIR) spectra were processed by GA after MSC or SNV, and four PLS calibration models were built by using the optimal combinations of these sub-regions. Meanwhile, the full region selecting PLS (Fr-PLS) models were developed. The R-SGA models variables were 434, 496, 310 and 496, for Early Red Comice, Wujiuxiang, Cascade and Kang Buddha, respectively. Despite the complexity of the spectral data, the R-SGA procedure was found to perform well (RMSEP = 0.428, 0.567 for Early Red Comice and Kang Buddha, respectively), leading to calibration models that significantly outperform those based on full-spectrum analyses (RM-SEP = 0.518, 0.633). The prediction precision of GA-PLS models was similar to that of Fr-PLS for Wujiuxiang and Cascade, with RMSEP of 0.696/0.694 and 0.425/0.421 respectively. This work proved that the R-SGA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization.

[Temperature compensation for calibration model of apple fruit soluble solids contents by near infrared reflectance].

The detection precision of soluble solids in apple fruit by near infrared reflectance (NIR) spectroscopy was affected by sample temperature. The NIR technique needs to be able to compensate for fruit temperature fluctuations. In the present study, it was observed that the sample temperature (2-42 degrees C) affects the NIR spectrum in a nonlinear way. The temperature model was built with R2 = 0.985, RMSEC = 1.88, and RMSEP = 2.32. When no precautions are taken, the error in the SSC reading may be as large as 2.55% degrees Brix. Two techniques were found well suited to control the accuracy of the calibration models for soluble solids with respect to temperature fluctuations, such as temperature variable--eliminating calibration model and global robust calibration model to cover the temperature range. And an improved genetic algorithms (GAs) was used to implement an automated variables selection procedure for use in building multivariate calibration models based on partial least squares regression (PLS). The two compensation methods were found to perform well with RMSEP1 = 0.72/0.69 and RMSEP2 = 0.74/0.68, respectively. This work proved that the compensation techniques could emend the temperature effect for NIR spectra and improve the precision of models for apple SSC by NIR.

[Region optimization of SSC model for Pyrus pyrifolia by genetic algorithm].

Genetic algorithm is widely used in NIRS data optimization, which is not limited by searching space. The region selecting by genetic algorithms (R-SGA)was applied in building calibration model of soluble solid content (SSC)of Pyrus pyrifolia, and the number of variables used to build calibration was further reduced from 2 075 to 690 in all of 3 models. Studies were performed to build GA-PLS models by different R-SGA latent variables, and the optimal R-SGA latent variables of Hosui, Wonhwang and Whangkeumbae pear were 10, 12 and 16, respectively. The R-SGA procedure was found to perform well (RMSEP = 0.608 and 0.524 for Hosui and Whangkeumbae pear respectively), leading to calibration models that significantly outperform those based on full-spectrum analyses (RMSEP = 0.632, 0.540). The prediction precision of GA-PLS models was similar to FULL-PLS for Wonhwang pear, with RMSEP of 0.610/0.595. In addition, the selected regions from R-SGA methods were used to build mixed model of 3 Pyrus pyrifolia varieties. The results indicated that the prediction precision of GA-PLS model was close to that of the full spectrum model, with RMSEP of 0.641 and 0.645, respectively. This work proved that the R-SGA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization, and it was feasible to build a universal model of different Pyrus pyrifolia varieties.

[Analysis of near infrared spectra of apple SSC by genetic algorithm optimization].

Genetic algorithm (GA) is an effective method in regions selection applied in building multivariate calibration model based on partial least squares regression. If genetic algorithm is run repeatedly as a block, the optimal solution is obtained faster, the numbers of data used to build calibration model are further reduced, and the prediction precision is further improved. An efficient method named region selecting by genetic algorithms (R-SGA) for building a PLS calibration model of NIR is presented in the present paper, in which each gene of chromosome represents a sub-region. In the R-SGA method, one needs to divide averagely the full spectral band into many sub-regions, and to build a research space with all the combinations of these sub-regions. The FT-NIR spectra were processed by GA after MSC and Savitky-Golay smoothing, a PL S calibration model of NIR wasbuilt by using the optimal combinations of these sub-regions. Meanwhile, the full region selecting PLS (FS-PLS) and experiential region selecting PLS (ES-PLS) models were developed using spectra after first-order derivative pretreatment. The seven intervals selected by region selecting by R-SGA which contained 434 variables were used as calibration set in GA-PLS. The prediction precision of GA-PLS model was better than FS-PLS and ES-PLS models, with Rc=0.966, RMSEC=0.469, Rv=0.954 and RMSEP=0.797. It was concluded that by using GA technique, in the pretreatment of apple SSC model by PLS, it is possible to optimize data selecting, enhance the precision of prediction and reduce the number of variables of calibration.

[Discrimination of brownheart and watercore of apples based on energy spectrum of visible/near infrared transmittance].

It is urgent to develop a quick and precise method for the discrimination of the internal quality of apple. Vis/NIR spectroscopy combined with multivariate analysis after the appropriate spectral data pre-treatment has been proved to be a very powerful tool for judgment of objects that have very similar exterior properties. In the present study, peak area discriminant analysis (PADA), principal component analysis discriminant analysis (PCADA) and partial least squares discriminant analysis (PLSDA) were applied to classify apples with different internal properties such as brownherat and watercore. Energy spectra were processed using MSC or one-order derivative, and three models using PADA, PCADA and PLSDA were built, respectively. The accuracy rates of prediction for brownheart apple were 100%, for watercore apple were 79.6%, 95.0% and 96.7%, and for natural apple were 88.4%, 98.2% and 98.8%, respectively. The PLSDA model was better than the others remarkably. And the overall correct ratio of PLSDA was 98.1%, with RMSEC = 0.449 and RMSEP = 0.392. The results in the present study show that Vis/NIR spectroscopy together with chemometrics techniques could be used to differentiate brownheart and watercore apple, which offers the benefit of avoiding time-consuming, costly and sensory analysis.

[Study on component detection of cheese wrapped by polyethylene film based on NIRS].

The possibility of direct determination of fat and protein of wrapped cheese by near infrared spectroscopy was studied. The influences of polyethylene film on the spectra were discussed in order to detect the components of wrapped cheese. And the influences were eliminated using Norris derivation filter pretreatment means. The models for fat and protein of wrapped cheese were calibrated by partial least squares regression (PLS) following eliminating outline, spectral pretreatment, and PLS factors optimization. The best models gave standard errors for calibration of 0.240 and 0.355, standard errors for prediction of 0.326 and 0.219, and correlation coefficients of 0.928 and 0.952 for fat and protein of wrapped cheese, respectively. The results showed no difference from those by non-wrapped cheese's models, and were better than wrapped cheese's models without Norris derivation filter pretreatment. Based on the results, it was concluded that near infrared spectroscopy is a reliable, accurate and fast method for non-invasive measurement of wrapped cheese fat and protein.