農(nóng)產(chǎn)品品質(zhì)檢測(cè)儀F-750是一款用于分析與農(nóng)產(chǎn)品品質(zhì)密切相關(guān)的農(nóng)產(chǎn)品內(nèi)部及外部特性的測(cè)量?jī)x器。
NIR（近紅外測(cè)定）技術(shù)在成套設(shè)備中的應(yīng)用可為我們提供客觀量化的質(zhì)量標(biāo)準(zhǔn)，已在生產(chǎn)中應(yīng)用多年。我們便攜式供電設(shè)備把近紅外分析技術(shù)帶給田間種植者，為作物收割前提供更好、更一致的作物成熟度的測(cè)定。
F-750可進(jìn)行物質(zhì)的定量估算（如葉綠素）、確定多種物質(zhì)的特性（如成熟度、TSS可溶性固形物、DM糖）并進(jìn)行定性分析（如風(fēng)味指數(shù)、個(gè)人偏好指數(shù)）。
主要功能
針對(duì)農(nóng)場(chǎng)品的品質(zhì)進(jìn)行檢測(cè)
快速測(cè)量（4～6秒）
非破壞測(cè)量
帶全球定位系統(tǒng)，便于裁剪制圖
野外可視半透顯示屏
充電/更換電池
SD卡數(shù)據(jù)存儲(chǔ)
可創(chuàng)建特殊品種的模型
收獲前成熟度評(píng)估
采后質(zhì)量檢驗(yàn)
測(cè)量參數(shù)
可測(cè)量可溶性固形物（糖度）、干物質(zhì)、內(nèi)部顏色、外部顏色、可滴定酸等指標(biāo)
應(yīng)用領(lǐng)域
主要應(yīng)用于果實(shí)成熟度和甜度相關(guān)參數(shù)的無(wú)損評(píng)估，包括田間作物管理和收獲期評(píng)估、果實(shí)儲(chǔ)藏、果實(shí)催熟及果實(shí)零售的各個(gè)環(huán)節(jié)。
主要技術(shù)參數(shù)
光譜儀：卡爾蔡司MMS-1光譜儀
光譜范圍：310-1100 nm
光譜樣點(diǎn)大小: 3 nm
光譜分辨率：8-13 nm
光源：鎢氙燈
鏡頭：鍍膜增益近紅外線鏡頭
快門(mén)：鍍金參考標(biāo)準(zhǔn)
顯示：光下可見(jiàn)液晶屏
PC接口：USB SD卡
記錄每次測(cè)量參數(shù)：原始數(shù)據(jù)，反射，吸收，一階導(dǎo)數(shù)吸收，二階導(dǎo)數(shù)吸光度數(shù)據(jù)
電源：可拆卸3100毫安時(shí)鋰離子電池
電池壽命：大于1600次
數(shù)據(jù)存儲(chǔ)：可拆卸32GB SD卡
機(jī)箱：電鍍鋁
尺寸：18×12×4.4cm
重量：1.05 kg
工作流程
構(gòu)建模型
F-750農(nóng)產(chǎn)品質(zhì)量測(cè)定儀可以對(duì)10-200種水果的品質(zhì)進(jìn)行測(cè)定；
利用可選擇性測(cè)量方法非破壞性測(cè)量每種水果的質(zhì)量參數(shù)（如：利用折射計(jì)測(cè)定白利糖度）；
內(nèi)置建模軟件可結(jié)合步驟1和步驟2的測(cè)量數(shù)據(jù)創(chuàng)建新的模型；
F-750可利用新創(chuàng)建的模型對(duì)感興趣參數(shù)進(jìn)行無(wú)損估計(jì)；
F-750可以使用多種模型創(chuàng)建自定義質(zhì)量指標(biāo)，例如：結(jié)合受試農(nóng)產(chǎn)品的糖度、顏色、酸度和干物質(zhì)量等綜合指標(biāo)可確定受試農(nóng)產(chǎn)品的食用質(zhì)量指標(biāo)。
計(jì)算測(cè)量值請(qǐng)鍵入文字或網(wǎng)站地址，或者上傳文檔。
與傳統(tǒng)光譜儀（利用光譜波段的比值進(jìn)行計(jì)算）不同，F(xiàn)-750利用使用者或軟件選取在310-1100nm光譜范圍內(nèi)的光譜集建立PLS模型進(jìn)行計(jì)算。
軟件模型采用非線性迭代偏最小二乘回歸（NIPLS）建立權(quán)重系數(shù)來(lái)衡量已知參數(shù)與不同波長(zhǎng)間的關(guān)系。F-750可計(jì)算出樣品的二階導(dǎo)數(shù)光譜并應(yīng)用各波長(zhǎng)的權(quán)重系數(shù)獲取實(shí)際測(cè)量圖譜。
量化的測(cè)量精度 (確定測(cè)量精度)
F-750結(jié)合了所選光譜集與樣品光譜間的實(shí)際差異，以及預(yù)期權(quán)重系數(shù)，從而為每次測(cè)量提供了一個(gè)精確的置信度。）
選購(gòu)指南
主機(jī)、說(shuō)明書(shū)、葉夾 箱子和相關(guān)配件
基本配置
應(yīng)用實(shí)例
產(chǎn)地：美國(guó)Felix
參考文獻(xiàn)
原始數(shù)據(jù)來(lái)源：Google Scholar
1. V.A. McGlone, R.B. Jordan, R.J. Seelye, C.J. Clark (2003) Dry-matter – a better predictor of the post-storage soluble solids in apples? Postharvest Biol. Technol., 28, pp. 431–435
2. P. Subedi1, K. Walsh1, and P. Purdy2 (2010) Determination of Optimum Maturity Stages of Mangoes Using Fruit Spectral Signatures, China Int Mango Conf 1-12
3. M. Cecilia Rousseaux, Juan P. Benedetti, Peter S. Searles (2008) Handheld NIR and grape fruit quality. 1-2
4. Kerry B. WalshAC, John A. GuthrieB and Justin W BurneyA (2000) Aust. J Application of commercially available, lowcost,miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit. Plant Physiol, 27: 1175-1186
5. P.P. Subedi a, K.B. Walsh a, G. Owensb (2007) Prediction of mango eating quality at harvest using short-wavenear infrared spectrometry. Postharvest Biology and Technology, 43: 326–334
6. Kerry B.Walsh1, Robert L. Long1 and Simon G. Middelton2 (2007) Use of near infra-red spectroscopy in evaluation of source-sink manipulation to increase the soluble sugar content of stonefruit. Journal of Horticultural Science & Biotechnology, (82:2) 316–322
7. Downey, G. (1996) measured NIR interactance (700–1100) spectra from six selected sites on the dorsal and ventral surfaces of each fish side on farmed salmon, resulting in 294 spectra from different sites The measurements were done through skin and scales by an unspecified fiber-optic interactance probe. Referencechemical values of fat and moisture were determined from excised flesh from the different NIR measurement sites. Fat ranges for the sites were 2.3–23.0% and moisture 57.9–74.7%. Spectral measurements on the dorsal surface gave lowest prediction errors (bias corrected) for fat 2.0% and moisture 1.45%.” Non-invasive and non-destructive percutanous analysis of farmed salmon flesh by near infrared spectroscopy. Food Chem. 55:305–311.
8. Cozzolino, D., Parker, M., Dambergs, R. G., Herderich, M. and Gishen, M. (2006) AIn the Vis region (400–700 nm) the spectra with very low absorption were those from Day 0 of the fermentations, that is, grape must before fermentation commenced. Samples taken after Day 0 showed a marked increase in anthocyanin absorption around 540 nm, thus demonstrating the extraction of these phenolic pigments from grape skins into the wine as the fermentaition proceeded.Chemometrics and visible-near infrared spectroscopic monitoring of red wine fermentation in a pilot scale. Biotechnol.Bioeng., 95: 1101–1107. doi: 10.1002/bit.21067
9. Phul P. Subedi a , Kerry B. Walsh a , and David. W. Hopkins b (2012) Assessment of titratable acidity in fruit using short wave near infrared spectroscopy. Part B: intact fruit studies. Near Infrared Spectrosc., (20) 459-463
