[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

[Study on Quality Identification of Olive Oil Based on Near Infrared Spectra].

Currently on the market, the sale of olive oil is mainly divided into extra virgin olive oil and common virgin olive oil. In order to identify the qualities of two different olive oils, a new method to identify the quality of olive oil with siPLS-IRIV-PCA algorithm is developed. Based on the near infrared spectral data of olive oil, the efficient spectral subintervals are selected with a synergy interval partial least squares (siPLS). The performance of the model is evaluated by using the root mean square error of cross-validation (RMSECV). The characteristic wavelengths are selected from the efficient spectral subintervals by iteratively retains informative variables (IRIV) algorithm. Principal component analysis (PCA) model is constructed based on the selected characteristic wavelengths. The samples of 90 groups of extra virgin olive oil and 90 groups of common olive oil are identified. PCA uses 1 427 wavelength variables as input variables and the contribution rates of the first two principal components are 51.891 8% and 26.473 2% respectively. siPLS-PCA uses 408 wavelength variables as input variables and the contribution rates of the first two principal components are 56.039 1% and 36.2355%. siPLS-IRIV-PCA uses 6 wavelength variables as input variables and the contribution rates of the first two principal components are 66.347 6% and 32.3043%. The result shows that, compared with PCA and siPLS-PCA, siPLS-IRIV-PCA has the best identification performance. The method is simple and convenient and has a high identification degree which offers a new approach to identify the quality of olive oil.

Recent advances in electronic nose techniques for monitoring of fermentation process.

Microbial fermentation process is often sensitive to even slight changes of conditions that may result in unacceptable end-product quality. Thus, the monitoring of the process is critical for discovering unfavorable deviations as early as possible and taking the appropriate measures. However, the use of traditional analytical techniques is often time-consuming and labor-intensive. In this sense, the most effective way of developing rapid, accurate and relatively economical method for quality assurance in microbial fermentation process is the use of novel chemical sensor systems. Electronic nose techniques have particular advantages in non-invasive monitoring of microbial fermentation process. Therefore, in this review, we present an overview of the most important contributions dealing with the quality control in microbial fermentation process using the electronic nose techniques. After a brief description of the fundamentals of the sensor techniques, some examples of potential applications of electronic nose techniques monitoring are provided, including the implementation of control strategies and the combination with other monitoring tools (i.e. sensor fusion). Finally, on the basis of the review, the electronic nose techniques are critically commented, and its strengths and weaknesses being highlighted. In addition, on the basis of the observed trends, we also propose the technical challenges and future outlook for the electronic nose techniques.

Design and experimental validation for direct-drive fault-tolerant permanent-magnet vernier machines.

A fault-tolerant permanent-magnet vernier (FT-PMV) machine is designed for direct-drive applications, incorporating the merits of high torque density and high reliability. Based on the so-called magnetic gearing effect, PMV machines have the ability of high torque density by introducing the flux-modulation poles (FMPs). This paper investigates the fault-tolerant characteristic of PMV machines and provides a design method, which is able to not only meet the fault-tolerant requirements but also keep the ability of high torque density. The operation principle of the proposed machine has been analyzed. The design process and optimization are presented specifically, such as the combination of slots and poles, the winding distribution, and the dimensions of PMs and teeth. By using the time-stepping finite element method (TS-FEM), the machine performances are evaluated. Finally, the FT-PMV machine is manufactured, and the experimental results are presented to validate the theoretical analysis.

Design and analysis of linear fault-tolerant permanent-magnet vernier machines.

This paper proposes a new linear fault-tolerant permanent-magnet (PM) vernier (LFTPMV) machine, which can offer high thrust by using the magnetic gear effect. Both PMs and windings of the proposed machine are on short mover, while the long stator is only manufactured from iron. Hence, the proposed machine is very suitable for long stroke system applications. The key of this machine is that the magnetizer splits the two movers with modular and complementary structures. Hence, the proposed machine offers improved symmetrical and sinusoidal back electromotive force waveform and reduced detent force. Furthermore, owing to the complementary structure, the proposed machine possesses favorable fault-tolerant capability, namely, independent phases. In particular, differing from the existing fault-tolerant machines, the proposed machine offers fault tolerance without sacrificing thrust density. This is because neither fault-tolerant teeth nor the flux-barriers are adopted. The electromagnetic characteristics of the proposed machine are analyzed using the time-stepping finite-element method, which verifies the effectiveness of the theoretical analysis.

[A wavelength selection approach of near infrared spectra Based on SCARS strategy and its application].

According to the characteristics of near infrared spectral(NIR)data, a new tactic called stability competitive adaptive reweighted sampling (SCARS) is employed to select characteristic wavelength variables of NIR data to build PLS model. This method is based on the stability of variables in PLS model. SCARS algorithm consists of a number of loops. In each loop, the stability of each corresponding variable is computed at first. Then enforced wavelength selection and adaptive reweighted sampling (ARS) is used to select important variables according to the stability of variables. The selected variables are kept as a variable subset and further used in the next loop. After the running of all loops, a number of subsets of variables are obtained and root mean squared error of cross validation (RMSECV) of PLS models is computed. The subset of variables with the lowest RMSECV is considered as the optimal variable subset. Validated by NIR data set of protein fodder solid-state fermentation process, the SCARS-PLS prediction model is better than PLS models based on wavelengths selected by competitive adaptive reweighted sampling (CARS) and Monte Carlo uninformative variable elimination (MC-UVE) methods. As a result, twenty one wavelength variables are selected by SCARS method to build the PLS prediction model with the predicted root mean square error (RMSEP) valued at 0.0543 and correlation coefficient (Rp) 0.9908. The results show that SCARS tactic can efficiently improve the accuracy and stability of NIR wavelength variables selection and optimize the precision of prediction model in solid-state fermentation process. The SCARS method has a certain application value.

Inferring Gene Regulatory Networks via Ensemble Path Consistency Algorithm Based on Conditional Mutual Information.

Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal due to the curse of dimensionality and the impact of external noise. In order to solve this problem, we introduce a novel algorithms called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), whose threshold of mutual information is dynamically self-adjusted. We first use principal component analysis to decompose a large-scale network into several subnetworks. Then, according to the absolute value of coefficient of each principal component, we could remove a large number of unrelated nodes in every subnetwork and infer the relationships among these selected nodes. Finally, all inferred subnetworks are integrated to form the structure of the complete network. Rather than inferring the whole network directly, the influence of a mass of redundant noise could be weakened. Compared with other related algorithms like MRNET, ARACNE, PCAPMI and PCACMI, the results show that EPCACMI is more effective and more robust when inferring gene regulatory networks with more nodes.

Rapid determination of pH in solid-state fermentation of wheat straw by FT-NIR spectroscopy and efficient wavelengths selection.

In the work discussed in this paper we investigated the feasibility of determination of the pH of a fermented substrate in solid-state fermentation (SSF) of wheat straw. Fourier-transform near-infrared (FT-NIR) spectroscopy was combined with an appropriate multivariate method of analysis. A genetic algorithm and synergy interval partial least-squares (GA-siPLS) were used to select the efficient spectral subintervals and wavelengths by k-fold cross-validation during development of the model. The performance of the final model was evaluated by use of the root mean square error of cross-validation (RMSECV) and correlation coefficient (R (c)) for the calibration set, and verified by use of the root mean square error of prediction (RMSEP) and correlation coefficient (R (p)) for the validation set. The experimental results showed that the optimum GA-siPLS model was achieved by use of seven PLS factors, when four spectral subintervals were selected by siPLS and then 45 wavelength variables were chosen by use of the GA. The predicted precision of the best model obtained was: RMSECV = 0.0583, R (c) = 0.9878, RMSEP = 0.0779, and R (p) = 0.9779. Finally, the superior performance of the GA-siPLS model was demonstrated by comparison with four other PLS models. The overall results indicated that FT-NIR spectroscopy can be successfully used for measurement of pH in solid-state fermentation, and use of the GA-siPLS algorithm is the best means of calibration of the model.

[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.

A novel PM motor with hybrid PM excitation and asymmetric rotor structure for high torque performance.

This paper proposes a novel permanent magnet (PM) motor for high torque performance, in which hybrid PM material and asymmetric rotor design are applied. The hybrid PM material is adopted to reduce the consumption of rare-earth PM because ferrite PM is assisted to enhance the torque production. Meanwhile, the rotor structure is designed to be asymmetric by shifting the surface-insert PM (SPM), which is used to improve the torque performance, including average torque and torque ripple. Moreover, the reasons for improvement of the torque performance are explained by evaluation and analysis of the performances of the proposed motor. Compared with SPM motor and V-type motor, the merit of high utilization ratio of rare-earth PM is also confirmed, showing that the proposed motor can offer higher torque density and lower torque ripple simultaneously with less consumption of rare-earth PM.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm.

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.

Long-living lotus: germination and soil {gamma}-irradiation of centuries-old fruits, and cultivation, growth, and phenotypic abnormalities of offspring.

Sacred lotus (Nelumbo nucifera) has been cultivated as a crop in Asia for thousands of years. An ∼1300-yr-old lotus fruit, recovered from an originally cultivated but now dry lakebed in northeastern China, is the oldest germinated and directly (14)C-dated fruit known. In 1996, we traveled to the dry lake at Xipaozi Village, China, the source of the old viable fruits. We identified all of the landmarks recorded by botanist Ichiro Ohga some 80 yr ago when he first studied the deposit, but found that the fruits are now rare. We (1) cataloged a total of 60 lotus fruits; (2) germinated four fruits having physical ages of 200-500 yr by (14)C dating; (3) measured the rapid germination of the old fruits and the initially fast growth and short dormancy of their seedlings; (4) recorded abnormal phenotypes in their leaves, stalks, roots, and rhizomes; (5) determined γ-radiation of ∼2.0 mGy/yr in the lotus-bearing beds; and (6) measured stratigraphic sequences of the lakebed strata. The total γ-irradiation of the old fruits of 0.1-3 Gy (gray, the unit of absorbed dosage defined as 1 joule/kg; 1 Gy = 100 rad), evidently resulting in certain of the abnormal phenotypes noted in their seedlings, represents the longest natural radiobiology experiment yet recorded. Most of the lotus abnormalities resemble those of chronically irradiated plants exposed to much higher irradiances. Though the chronic exposure of the old fruits to low-dose γ-radiation may be responsible in part for the notably weak growth and mutant phenotypes of the seedlings, it has not affected seed viability. All seeds presumably repair cellular damage before germination. Understanding of repair mechanisms in the old lotus seeds may provide insight to the aging process applicable also to other organisms.